The DOI matches its Crossref record, with a small difference in title or year; 31 of the 31 references it cites were found in a registry or an index; 2 of the 14 sources its claims cite were checked for support.
ImageNet classification with deep convolutional neural networks
Open accessAbstract
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
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Claims the paper makes
29 No basis given · 22 Own result · 19 Own interpretation · 4 No full text of the sources · 3 Sources not found · 2 Supported · 2 Not checked yet · 1 Own method
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In the paper
“This technique reduces complex co adaptations of neurons, since a neuron cannot rely on the presence of particular other neurons.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper states on p. 1 that dropout prevents complex co-adaptations by randomly omitting hidden units, so a hidden unit cannot rely on other hidden units being present. This directly supports the citing sentence. (LLM, conf=0.95)
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In the paper
“There is, however, a very efficient version of model combination that only costs about a factor of two during training.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper describes dropout as an efficient way to combine many neural-network models (p. 1). In F.6 Training (p. 17), it reports that ImageNet training took roughly four days with dropout versus two days without it, supporting the approximate factor-of-two training cost. (LLM, conf=0.88)
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In the paper
“This scheme reduces our top-1 and top-5 error rates by 1.7% and 1.2%, respectively, as compared with a net with half as many kernels in each convolutional layer trained on one GPU.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.2. Training on multiple GPUs
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In the paper
“The size of our network made overfitting a significant problem, even with 1.2 million labeled training examples”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“So these data augmentation schemes are, in effect, computationally free.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
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In the paper
“we found that removing any convolutional layer (each of which contains no more than 1% of the model's parameters) resulted in inferior performance.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“This scheme reduces the top-1 and top-5 error rates by 0.4% and 0.3%, respectively, as compared with the non overlapping scheme s = 2, z = 2, which produces output of equivalent dimensions.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
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In the paper
“This increases the size of our training set by a factor of 2048”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
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In the paper
“Deep CNNs with ReLUs train several times faster than their equivalents with tanh units.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.1. Rectified Linear Unit nonlinearity
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In the paper
“However, we still find that the following local normalization scheme aids generalization.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
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In the paper
“Although the 1000 classes of ILSVRC make each training example impose 10 bits of constraint on the mapping from image to label, this turns out to be insufficient to learn so many parameters without considerable overfitting.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5. REDUCING OVERFITTING
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In the paper
“though the resulting training examples are, of course, highly inter dependent.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
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In the paper
“On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively”
§ ImageNet Classification with Deep Convolutional Neural Networks > Abstract
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In the paper
“We generally observe during training that models with overlapping pooling find it slightly more difficult to overfit.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
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In the paper
“a four-layer CNN achieved a 13% test e r r o r r a t e w i t h o u t n o r m a l i z a t i o n a n d 1 1 % w i t h normalization.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
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In the paper
“so we used several effective techniques for preventing overfitting”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“Response normalization reduces our top-1 and top-5 error rates by 1.4% and 1.2%, respectively.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
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In the paper
“The two-GPU net takes slightly less time to train than the one-GPU net.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.2. Training on multiple GPUs
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In the paper
“Our network contains a number of new and unusual features which improve its performance and reduce its training time”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“Without this scheme, our network suffers from substantial overfitting, which would have forced us to use much smaller networks.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
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In the paper
“For example, only other types of cat are considered plausible labels for the leopard.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
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In the paper
“This initialization accelerates the early stages of learning by providing the ReLUs with positive inputs.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 6. DETAILS OF LEARNING
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In the paper
“Notice that at the pixel level, the retrieved training images are generally not close in L2 to the query images in the first column.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
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In the paper
“Training one CNN, with an extra sixth convolutional layer over the last pooling layer, to classify the entire ImageNet Fall 2011 release (15M images, 22K categories), and then”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Our top-1 and top-5 error rates on this dataset are 67.4% and 40.9%, attained by the net described above but with an additional, sixth convolutional layer over the last pooling layer.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
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- Rests on an assumption: The Fall 2009 ImageNet split used by the authors, which differs from previous authors' splits, does not affect the results appreciably.
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In the paper
“This kind of specialization occurs during every run and is independent of any particular random weight initialization (modulo a renumbering of the GPUs).”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
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In the paper
“The magnitude of the effect demonstrated here varies with network architecture, but networks with ReLUs consistently learn several times faster than equivalents with saturating neurons.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 9. EPILOGUE
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In the paper
“Our network achieves top-1 and top-5 test set error rates of 37.5% and 17.0%, respectively.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 7. RESULTS
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In the paper
“Averaging the predictions of five similar CNNs gives an error rate of 16.4%.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
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In the paper
“In the remainder of this paragraph, we use validation and test error rates interchangeably because in our experience they do not differ by more than 0.1% (see Table 2).”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
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In the paper
“Notice that even off-center objects, such as the mite in the top-left, can be recognized by the net.”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
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In the paper
“All of our experiments suggest that our results can be improved simply by waiting for faster GPUs and bigger datasets to become available.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“ReLUs have the desirable property that they do not require input normalization to prevent them from saturating.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
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None of the sources cited for this claim have been checked yet.
In the paper
“Convolutional neural networks (CNNs) constitute one such class of models.9,15,17,19,21,26,32”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites [9] 9. Fukushima, K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36, 4 (1980), 193-202. source
- Not checked yetcites [15] 15. Krizhevsky, A. Convolutional deep belief networks on cifar-10. Unpublished manuscript, 2010.
- Not checked yetcites [17] 17. LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., Jackel, L., et al. Handwritten digit recognition with a back-propagation network. In Advances in Neural Information Processing Systems (1990).
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In the paper
“However, the immense complexity of the object recognition task means that this problem cannot be specified even by a dataset as large as ImageNet, so our model should also have lots of prior knowledge to compensate for all the data we do not have.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Thus, compared to standard feedforward neural networks with similarly sized layers, CNNs have much fewer connections and parameters and so they are easier to train, while their theoretically best performance is likely to be only slightly worse.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“methods that require careful hand-engineering by a programmer who understands the domain do not scale as well as methods that replace the programmer with a powerful general-purpose learning procedure.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 1. PROLOGUE
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In the paper
“Luckily, current GPUs, paired with a highly optimized implementation of 2D convolution, are powerful enough to facilitate the training of interestinglylarge CNNs”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“they also make strong and mostly correct assumptions about the nature of images (namely, stationarity of statistics and locality of pixel dependencies).”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“Despite the attractive qualities of CNNs, and despite the relative efficiency of their local architecture, they have still been prohibitively expensive to apply in large scale to highresolution images.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
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In the paper
“ImageNet, 7 which consists of over 15 million labeled high-resolution images in over 22,000 categories.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites [7] 7. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L. ImageNet: A large-scale hierarchical image database. In CVPR09 (2009). source
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In the paper
“which is considerably better than the previous state-of-the-art”
§ ImageNet Classification with Deep Convolutional Neural Networks > Abstract
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In the paper
“With enough computation and enough data, learning beats programming for complicated tasks that require the integration of many different, noisy cues.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 1. PROLOGUE
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“and recent datasets such as ImageNet contain enough labeled examples to train such models without severe overfitting.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“for deep neural networks to shine, they needed far more labeled data and hugely more computation.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 1. PROLOGUE
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If at least some training examples produce a positive input to a ReLU, learning will happen in that neuron.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To learn about thousands of objects from millions of images, we need a model with a large learning capacity.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The constants k, n, α, and β are hyperparameters whose values are determined using a validation set”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.3. Local response normalization
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, on this dataset the primary concern is preventing overfitting, so the effect they are observing is different from the accelerated ability to fit the training set which we report when using ReLUs.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.1. Rectified Linear Unit nonlinearity
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“since then the best published results are 45.7% and 25.7% with an approach that averages the predictions of two classifiers trained on Fisher Vectors (FVs) computed from two types of densely sampled features.29”
§ ImageNet Classification with Deep Convolutional Neural Networks > 7. RESULTS
What the sources say
- Not checked — no full text of the sourcecites [29] 29. Sánchez, J., Perronnin, F. High- dimensional signature compression for large-scale image classification. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011 (2011). IEEE, 1665-1672. source
plain- Asserted: passes — The authors put it forward as true.
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A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Combining the predictions of many different models is a very successful way to reduce test errors, 1, 3”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“but it appears to be too expensive for big neural networks that already take several days to train.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim.
In the paper
“The best published results on this dataset are 78.1% and 60.9%.23”
§ ImageNet Classification with Deep Convolutional Neural Networks > Model
What the sources say
- Not checked — no full text of the sourcecites [23] 23. Mensink, T., Verbeek, J., Perronnin, F., Csurka, G. Metric learning for large scale image classification: Generalizing to new classes at near-zero cost. In ECCV -European Conference on Computer Vision (Florence, Italy, Oct. 2012). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
Objects in realistic settings exhibit considerable variability.
“But objects in realistic settings exhibit considerable variability”
The paper relies on this without support.
Object identity is invariant to changes in the intensity and color of illumination.
“object identity is invariant to changes in the intensity and color of the illumination.”
The paper relies on this without support.
The Fall 2009 ImageNet split used by the authors, which differs from previous authors' splits, does not affect the results appreciably.
“Since there is no established test set, our split necessarily differs from the splits used by previous authors, but this does not affect the results appreciably.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the field of DL, the CNN is the most famous and commonly employed algorithm [30, 71–75].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Fully convolutional neural networks (fCNNs) have provided state-of-the-art performance for object classification and image segmentation (1).”
From Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning (2018) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The first wave saw the emergence of deep learning as the dominant paradigm for analyzing high-dimensional biological data.”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [1]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Krizhevesky et al. [30] first proposed AlexNet and consequently improved the CNN learning ability by increasing its depth and implementing several parameter optimization strategies.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
The cited source says what the claim attributes to it.
In the citing paper
“In deep CNN architecture, AlexNet is highly respected [30], as it achieved innovative results in the fields of image recognition and classification.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
The cited source says what the claim attributes to it.
In the citing paper
“Many CNN models, e.g. AlexNet [30], GoogleNet [103], and ResNet [37], have been trained on large datasets such as ImageNet for image recognition purposes.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
The cited source says what the claim attributes to it.
In the citing paper
“To avoid model-specific biases, two different AI models, AlexNet [22] and VGG16 [38], were selected for classification.”
From Study on the Helpfulness of Explainable Artificial Intelligence (2024) · cites this paper as [22]
The cited source says what the claim attributes to it.
In the citing paper
“In particular, the choice of this specific size is based on the fact that the AlexNet 34 (and also similar CNN architectures) has been shown to offer satisfactory performances with an input matrix of that size or very similar, albeit for different types of problems with respect to the one presented in this work (e.g., image classification).”
The cited source says what the claim attributes to it.
In the citing paper
“Jung et al. [21] found that a reduction in accuracy does not occur when using only four bits for both weight and activation parameters, even for the ImageNet dataset [32].”
From Zero-Centered Fixed-Point Quantization With Iterative Retraining for Deep Convolutional Neural Network-Based Object Detectors (2021) · cites this paper as [32]
The cited source says what the claim attributes to it.
In the citing paper
“Feature-based approaches [17,18,19] commonly employ networks pre-trained on ImageNet [20] to map original images into a more distinguishable feature space. These methods are typically characterized by low training costs and outstanding performance.”
From An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder (2024) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“Over the past several years, the successes of deep learning on visual tasks has critically relied on convolutional neural networks [20, 16].”
From Do Vision Transformers See Like Convolutional Neural Networks? (2021) · cites this paper as [20]
The cited source says what the claim attributes to it.
In the citing paper
“A common perception metric is the Learned Perceptual Image Patch Similarity (LPIPS) which uses pre-trained deep neural network backbones such as VGG [9] and AlexNet [22].”
From MTReD: 3D Reconstruction Dataset for Fly-over Videos of Maritime Domain (2025) · cites this paper as [22]
The cited source says what the claim attributes to it.
In the citing paper
“Starting with the the success of AlexNet [5] in the ILSVRC challenge [6] in 2012, deep learning models have been outperforming traditional approaches on a number of computer vision problems.”
From Satellite Pose Estimation Challenge: Dataset, Competition Design, and Results (2020) · cites this paper as [5]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Unfortunately, it has been acknowledged that the performance of those hashing schemes may degrade significantly if the projected dimensions are imbalanced [4], [6], [13], i.e., their variances vary a lot.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [6]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“When convolutional RBMs are stacked together, they form convolutional deep belief networks [22].”
From Multimodal emotion recognition using deep learning architectures (2016) · cites this paper as [22]
Experimental — accuracy not yet measuredIn the citing paper
“Convolutional neural networks (CNNs), built for spatial data, have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation [15,16,17,18,19,20].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [16]
In the citing paper
“When a low-capacity model, such as AlexNet, is used for pre-training, little improvement can be obtained when compared to the Resnet50 family [26].”
From Self-supervised learning: A succinct review (2023) · cites this paper as [26]
In the citing paper
“Since its introduction by Krizhevsky et al. [7] in 2012, the CNN model has been successfully applied to a wide range of multimodal applications, such as image dehazing [239, 240] and human activity recognition [241].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [7]
In the citing paper
“The AlexNet architecture was published in [4], achieved significantly improved performance over the other non-deep learning methods for ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [4]
In the citing paper
“Deep CNN architectures with 8, even 22 layers [4], [33], can be useful even for CADe problems where the available training datasets are limited.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [4]
In the citing paper
“focusing on AI and deep learning architectures that have already become the de facto approach.”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [101]
In the citing paper
“Since then, fueled by the revolutionary results of AlexNet [101] and incarnations of patch-based adaptations of Deep Boltzmann Machines and stacked autoencoders, deep learning based segmentation of anatomy and pathology has witnessed a revolution (see also Table II), where for some tasks now we observe human level performance [102].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [101]
In the citing paper
“ReLU significantly increases the rate of con-vergence of stochastic gradient descent in comparison with the sigmoid [26].”
From Real-time system for automatic cold strip surface defect detection (2019) · cites this paper as [26]
In the citing paper
“Real-time volume estimation has been achieved using fully convolutional neural networks (FCNNs) [11] to produce state-of-the-art performance in a range of medical imaging modalities [12].”
From Fully Automated 3-D Ultrasound Segmentation of the Placenta, Amniotic Fluid, and Fetus for Early Pregnancy Assessment (2021) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
“we found that removing any convolutional layer (each of which contains no more than 1% of the model's parameters) resulted in inferior performance.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.5. Overall architecture
“The third convolutional layer has 384 kernels of size 3 × 3 × 256”
Citations
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Going deeper with convolutions
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Cited by
Sorted by citations
Items 105,701–105,800 of 108,435
Deep Learning Models for Corn Kernel Counting and Yield EstimationPreprint
Ghimire, B., Lacerda, L. N., Bourlai, T., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5768641
A DNN-Oriented Rapid Simulation Framework for Digital Processing-In-Memory Architectures
Kan, Y., Zhu, G., Zhang, R., & Nakashima, Y. (2025) · IEEE International System-on-Chip Conference (SOCC) · doi:10.1109/socc66126.2025.11235475
“Deep neural networks (DNNs) have emerged as one of the most powerful models in the field of artificial intelligence, and have been widely applied to complex perception and cognitive tasks such as computer vision, speech recognition, and natural language processing [1]–[3].”
Syam, S., & Pushpalatha, K. P. (2025) · International Conference on Smart Computing and Communications (ICSCC) · doi:10.1109/icscc66177.2025.11233559
Faria, J. R., Coutinho, F. D. L., & Oliveira, A. S. R. (2025) · IEEE Nordic Circuits and Systems Conference (NorCAS) · doi:10.1109/norcas66540.2025.11231201
“CNNs typically comprise one or more convolutional layers between other deep learning layers [12], as shown in Fig.”
Point‐Guided Latent Diffusion Model for Novel View Synthesis in Laparoscopic Liver Surgery
Tang, W., Chen, T., Espinel, Y., et al. (2025) · Healthcare Technology Letters · doi:10.1049/htl2.70032
Fully automated IVUS image segmentation with efficient deep-learning-assisted annotation
Zhang, L., Chen, Z., Zhang, H., Zaman, F. A., Wahle, A., Wu, X., McNamara, C. A., Taylor, A. M., & Milan Sonka, A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111312
DL Based Approach for Assessing the Severity of DR from Retinal Fundus Images
Jeyaraj, S., & Nath, M. K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06253-6_14
Gul, M. J. J., Paul, A., & Singh, K. K. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3634569
“The modern surge in AI began with the success of CNNs on large-scale image classification, most notably AlexNet [1].”
A Series of Structural Head Loop Bottleneck Attention in Fault Diagnosis for Pumping Unit
Yang, Y. Q., Li, Y., Cen, H., et al. (2025) · doi:10.1109/iceiom65271.2025.11239932
“Initially, numerous classification models such as AlexNet[1], VGGNet[2], GoogLeNet[3] and ResNet[4] are widely used in image classification tasks.”
Short-Term Power Load Forecasting Based on SVMD and Enhanced Informer Model
Ding, N., Li, M., Zhou, C., et al. (2025) · International Conference on Energy and Electrical Power Systems (ICEEPS) · doi:10.1109/iceeps66790.2025.11239844
“Among these, convolutional neural network (CNN) [1], recurrent neural network (RNN) [2], and long short-term memory network (LSTM) [3] have demonstrated strong capabilities in capturing nonlinear relationships and temporal dependencies in electricity load data.”
Yang, X., Yu, Z., Tong, J., et al. (2025) · Advances in Structural Engineering · doi:10.1177/13694332251399290
“Additionally, a convolutional neural network (Krizhevsky et al., 2017) is employed to facilitate feature extraction.”
Vision-language tracking with attention-based optimization
Hu, S., Liu, T., Han, L., & Xing, R. (2025) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2025.104644
Designing and Implementing a Fake Document Detection System Using Artificial Intelligence
Maray, A. H., Abed, M. N., Sheet, N. S. M., & Hussein, R. A. (2025) · Journal of Engineering · doi:10.1155/je/3902628
Quan, L., Chen, S., Yang, Z., Ju, J., Lei, Z., Huang, Y., Qiao, G., Yue, J., He, P., Yang, J., et al. (2025) · Journal of the National Cancer Center · doi:10.1016/j.jncc.2025.11.002
A Comparison of CNN Architectures in Edge Computing Environments: Health Care Prospect
Yunos, N. S., Yogarayan, S., Ahmad, N. M., et al. (2025) · International Conference on Advanced Information Technologies (ICAIT) · doi:10.1109/icait68809.2025.11236802
“For instance, utilised edge detection and shape-based methods to discern face masks, noting their method's simplicity but also recognizing limitations in complex backgrounds and diverse mask types [10].”
Signal-Transformer: Multi-Channel Sensor-Based Tool Health Monitoring
Yin, Y., Yang, Z., Dao, J., et al. (2025) · doi:10.1109/icamtms66452.2025.11239742
“Since the introduction of AlexNet, deep learning models have been widely adopted in nonlinear system prediction tasks [6].”
From Tasks to Topology: Dorsal and Ventral Streams Emerge in Optimized Neural NetworksPreprint
Reza, T., Jordan, E., Luo, S. T. S., Patel, K. D., Tang, S. J., & Niemeier, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.16.688720
“These tasks are associated with more dorsal or ventral brain regions, respectively 27-30 , with grasping relying on three-dimensional structure derived from stereoscopic depth cues 34,35 .”
Wang, Z., Di, H., Zhang, R., & Liu, F. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109152 · Cited by 1
Sign language detection using deep learning
Costîn, I. A., & Mirea, L. (2025) · International Conference on System Theory, Control and Computing (ICSTCC) · doi:10.1109/icstcc66753.2025.11240436
“AlexNet , [10], the second architecture used, is a deep convolutional neural network consisting of 8 layers with learnable weights, 5 convolutional and 3 fully connected.”
Kumari, M., Yupapin, P. P., Ali, J., & Ray, K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-95-1069-6_22
Dam, V. A. T., & Kim, J. (2025) · Clinical & Translational Oncology · doi:10.1007/s12094-025-04119-1
The Asymmetry of Gender-Fluid Trends
Hoff, M., & Bellezza, S. (2025) · Journal of Consumer Research · doi:10.1093/jcr/ucaf060
Developing a CNN Model (Rebbica) for Effective Lung Cancer Classification on Histopathology Images
Adithya, N. N. S. S. S., Sah, P., Jyothirmai, B., et al. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-06677-0_13
Dual sparsity aware PE networks for CNN accelerators in edge AI deployments
Akshayraj, M. R., C., M. R. P., Gopi, V. P., et al. (2025) · International Journal of Information Technology · doi:10.1007/s41870-025-02903-4
Historical Evolution of Diagnostic Techniques
Hirosawa, T. (2025) · Springer eBooks · doi:10.1007/978-981-95-4338-0_2
Robust wavefront sensing method based on machine learning for optical thickness profiling
Kim, H. D., Kim, Y. ‐., Ito, Y., & Sugita, N. (2025) · Journal of Mechanical Science and Technology · doi:10.1007/s12206-025-2508-z
“The training network of the proposed method, wavefront sensing U-shaped network (WS-UNet), was developed by combining a U-shaped network [27] with robust CNN architecture [28] to accurately extract the coefficients values corresponding to Zernike polynomials from a single fringe pattern.”
A Bipartite Graph Approach to Linking Selection Marks and Text
Agarwal, A., Panda, S., & Pachauri, K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5860-2_1
ADMM-Enhanced CNN Training Framework With Global Convergence Guarantees
Song, C., Kong, Z., Liu, S., Ding, L., Huang, B., Xiang, W., & Huang, T. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3636117
Low-Overhead CNN Acceleration via FFT-Based Circular Convolution and Partially Pipelined Dataflow
Meng, Y., Wang, J.-F., Fu, Q., et al. (2026) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3636454
“S TATE-OF-THE-ART (SOTA) convolutional neural networks (CNNs) have demonstrated superior performance across diverse domains, including image classification [1], [2], [3], semantic segmentation [4], and object recognition [5].”
Wu, S., Pan, Q., Mingxin, F. U., Gao, Y., & Li, Y. (2025) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2025.103937 · Cited by 1
EFE-YOLO:A YOLO11n-based Efficient Feature Enhanced Framework for Steel Surface Defect DetectionPreprint
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Using deep learning methods to automatically interpret blood culture Gram stains
Yiş, R., Kocadurdu, K., & Berktaş, M. (2025) · Revista română de medicină de laborator · doi:10.2478/rrlm-2025-0027
“In the 2012 Large Scale Visual Recognition Competition (ImageNet-2012), the deep learning basic architecture convolutional neural network won the first prize, which further increased the popularity of deep learning [7].”
Liang, Z., Zhang, X., Liu, Z., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-8051962/v1
MultiGS: Multi-Dimensional Information-Aware Gradient Sparsification
Ma, J., Donglai, L., Xie, W., Li, H., Li, Y., & Fang, L. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3636885
Decoupling-Based Cross-Layer Connection Removal for Compact Remote Sensing Image Classification
Deng, Z., Han, Y., Wang, W., et al. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3636678
Hybrid Dual-Batch and Cyclic Progressive Learning for Efficient Distributed TrainingPreprint
Lu, K.-W., Hong, D., Liu, P., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5805848
Li, J., Zhao, Y., Jin, Y., Zheng, C., & Qiang, W. (2025) · Machine Learning · doi:10.1007/s10994-025-06927-6
“We train TMDC on UCF-101 (Soomro et al., 2012 ) and use two CaffeNets (Krizhevsky et al., 2017 ) for extracting features from images and optical flows by using the batch size of 128.”
Gaze Point Estimation via Joint Learning of Facial Features and Screen Projection
Zhang, Y., Xu, F., & Yang, Y. T. (2025) · Applied Sciences · doi:10.3390/app152312475
Adiga, S. R., & Zafar Ali Khan, N. (2025) · International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) · doi:10.1109/iccams65118.2025.11234037
“These preprocessing steps guarantee uniformity and enhance the model’s ability to generalize across diverse datasets, as supported by Krizhevsky et al. [18].”
DeepSarc: A Transformer‐Based Deep Learning Approach for Sarcasm Detection in Social Media Text
Haq, R., Zhang, X., Yitagesu, S., Khan, W., & Feng, Z. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70405 · Cited by 1
Cui, H., Shao, C., Wei, W., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5800073
Li, Y., Wang, B., He, R., Tang, H., & Li, Z. (2025) · Earthquake research advances · doi:10.1016/j.eqrea.2025.100441
Efficient multi-class fire detection using vision transformer
Somadas, S., Gounder, M. S., & Biradar, V. G. (2025) · AIP conference proceedings · doi:10.1063/5.0297411
AI-Ouran, R., Awad, M. A., Al-Samrraie, L. A., Taqieddin, E., Al-Zoubi, H., & Rousan, L. A. (2025) · IEEE Access · doi:10.1109/access.2025.3637589
SIM: Accelerating Distributed DNN Training by Exploring Gradient Similarity
Jin, Y., Li, Y., Li, W., et al. (2025) · IEEE Transactions on Networking · doi:10.1109/ton.2025.3633713
Forest Fire Recognition Neural Networks Based on Edge ComputingPreprint
He, L., Tang, Q., Jiwa, A., Li, J., Zeng, C., & Du, Z. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5813592
Huang, J., Sun, W., Wang, Y., & Wei, Y. (2025) · Journal of Asian Architecture and Building Engineering · doi:10.1080/13467581.2025.2594253
Go, I.-S., Fu, Y., Ma, X., et al. (2025) · npj Heritage Science · doi:10.1038/s40494-025-02163-2
Knowledge graph visualization of Artificial intelligence for lung disease diagnosis
Wu, W., Zhao, C., Song, J., et al. (2025) · doi:10.1145/3759972.3760143
“The most influential work continues to be that of Krizhevsky’s AlexNet and Chen’s computer-assisted diagnosis system, providing essential technical foundation-ings [23, 24].”
Performance Evaluation of CNN Models for ImageBased Medicinal Plant Classification
Martono, G. H., & Triwijoyo, B. K. (2025) · doi:10.1109/iceeie66203.2025.11252300
“Presented in [20] introduced in 2012, AlexNet was a breakthrough CNN architecture that significantly improved ImageNet classification accuracy and revived interest in deep learning for computer vision.”
Steel Strip Surface Defect Detection Based on EBi-YOLO11
Zhang, K., Han, Q.-P., Gao, A., et al. (2025) · 2025 International Conference on Computational Intelligence and Robotics (CIR) · doi:10.1109/cir65373.2025.11257241
“Since the rise of convolutional neural networks (CNNs) in 2012, architectures such as AlexNet [4] , VGG [5] , GoogLeNet [6] , and ResNet emerged successively, greatly promoting the development of deep learning-based detection methods grounded in convolutional neural networks.”
A Novel Runway Area Detection Network In Polsar Images Based On Semi - Supervised Learning
Han, P., Zhang, Z., Zhou, J., et al. (2025) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss55030.2025.11243329
Automatic Identification of Basal Units in Ice Sheets Based on Signal Feature and Weigth Control
Lang, S., Liu, C., Cui, X.-B., et al. (2025) · IEEE International Geoscience and Remote Sensing Symposium · doi:10.1109/igarss55030.2025.11243895
“We used convolutional layers to map stratigraphic features to candidate regions, batch normalized layers to normalize features of different magnitudes, compared loss operations to analyze particle concentration distributions in candidate regions [11].”
Detecting Methane in Satellite Images Using Deep Ensembles
Zieba, K., Nalepa, J., & Wijata, A. M. (2025) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss55030.2025.11243616
Radiological trends in convolutional neural networks for breast cancer diagnosis
Li, K. L., Tam, M. G. Z., Li, S. K., & Aftab, F. (2025) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2025.100322
A weld defect segmentation framework based on visual self-supervised learning
Cheng, H., Jiang, H., Zhang, Y., et al. (2025) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2025.2595520
Fabricio, V., Storch, I., Corrêa, G., et al. (2025) · Brazilian Symposium on Multimedia and the Web · doi:10.5753/webmedia.2025.15921
“The trend of using GPUs for general-purpose computing [11, 13, 20] is a result of their highly parallel architecture [17].”
Zhao, S., Guo, Z., Jiang, W., Lan, Y., & Ma, W. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132234
Jiang, Z., Zhang, J., Wang, K., & Si, C. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247258
Feasibility Analysis of real-time Robustness Certification
Seferis, E., & Kollias, S. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246579
“Deep Neural Networks (DNNs) have achieved remarkable performance across domains such as image and speech recognition [1], [2], language understanding [3], [4], and robotics [5].”
Tapia, R., Ijjeh, A. A., Dios, J. R. M., et al. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247230
“Convolutional Neural Networks (CNNs) learn robust, hierarchical representations directly from raw frame data, drastically boosting accuracy under challenging conditions [12].”
Hybrid Dual-Batch and Cyclic Progressive Learning for Efficient Distributed TrainingPreprint
Lu, K.-W., Hong, D., Liu, P., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5819197
Improved visually prompted keyword localisation in real low-resource settings
Nortje, L., Oneaţă, D., Pîrlogeanu, G., & Kamper, H. (2025) · International Conference on Speech Technology and Human-Computer Dialogue (SpeD) · doi:10.1109/sped67700.2025.11252119 · Cited by 1
Super-Resolved Structural MRI for Detection of Mild Cognitive Impairment
Grigas, O., Plonis, D., & Maskeliūnas, R. (2025) · IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) · doi:10.1109/aieee66149.2025.11050865
Research on tissue section negative detection algorithm based on multispectral microscopic imaging
Wang, C., Ge, Q., Wu, R.-J., Jian, H.-P., Chu, H., Yang, J., Chen, Q., Zhao, X., Xiang, H., & Zhang, D.-W. (2025) · Journal of Innovative Optical Health Sciences · doi:10.1142/s179354582550035x
Du, Y., Li, R., Xie, M., Jiang, Y., Chicas, S. D., Liu, J., & Lu, W. (2025) · AIMS environmental science · doi:10.3934/environsci.2025043
Tang, P., Wang, Y., & Men, A. (2025) · Sensors · doi:10.3390/s25237232 · Cited by 2
“To better reflect human visual preference, we also report the Learned Perceptual Image Patch Similarity (LPIPS) [50] on paired datasets, using the official implementation with the AlexNet [56] backbone.”
Uyar, T., Erdamar, A., & Erkoç, F. Ü. (2025) · The European Zoological Journal · doi:10.1080/24750263.2025.2587506
“These components enable the networks to identify edges, textures, object parts, and, ultimately, objects in images (Krizhevsky et al. 2017; Alzubaidi et al. 2021).”
Wang, Z., Miao, F., & Zhang, X. (2025) · Algorithms · doi:10.3390/a18120751
Efficient Hand Pose Estimation Using Transformer-Based Backbone and Multi-Scale Interactions
Chen, H., Wang, Y., Xie, Y., et al. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.11.207
DCB: An Efficient Approach for Building Long-Range Dependencies in CNNs
Lan, T., He, M., & Dai, Y. (2025) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc65261.2025.11249096
“Early CNNs like [1], [2], [4], [18]–[20] employ large-kernel convolution in shallow layers for low-level feature extraction.”
NeuraVis: Retina Damage Detection Using Transfer Learning
Mohamed, A., Ali, S. M., Yaseen, T., et al. (2025) · International Symposium on INnovations in Intelligent SysTems and Applications · doi:10.1109/inista68122.2025.11249636
Heidari, M., Moattar, M. H., & Ghaffari, H. R. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-08084-y
SiamMCA: infrared target tracking algorithm with mixed channel attention
Li, Y., Zhang, S., Diao, C., Fang, Y., Li, L., Zhao, Z., & Li, C. (2025) · Journal of Electromagnetic Waves and Applications · doi:10.1080/09205071.2025.2545276
“However, most of the above trackers use AlexNet [37] as the backbone network with limited feature extraction capability.”
MCMTNet: A Lightweight Dual-Stream Model for High-Accuracy WiFi-Based Human Activity Recognition
Li, Z., Yang, Z., Zhang, Y., et al. (2025) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-025-2874-9
FFIR-Net: a feature fusion and inter-stock relationship network for stock price movement prediction
Yu, X., Lv, Z., Chen, Y., Shen, C., & Chen, C. (2025) · International Journal of General Systems · doi:10.1080/03081079.2025.2590080
Bhatt, S. D., Astik, M. B., & Soni, H. B. (2025) · IEEE International Conference on Contemporary Computing and Communications (InC4) · doi:10.1109/inc465408.2025.11256319
Hybrid Deep Learning Model for Runoff Forecasting Based on CNN and LSTM
Wu, Y., Wang, D., Deng, Q., & Ye, X. (2025) · International Conference on Advanced Algorithms and Neural Networks (AANN) · doi:10.1109/aann66429.2025.11257702
“These advantages have enabled their extensive application in fields like computer vision [13].”
Chen, L., Shi, Z., Lum, L., Li, T., Wang, J., Zhou, H., Zee, B., Chin, J. M., & Lim, Y. K. (2025) · IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) · doi:10.1109/ipfa65338.2025.11256606
“This type of data does not conform to the spatial locality assumptions required by convolutional neural networks (CNNs) [17, 18], nor does it follow the temporal sequence patterns typically handled by recurrent models such as Long Short-Term Memory (LSTM) [19].”
Li, X., Yuan, G., Yu, P., et al. (2025) · International Journal of Remote Sensing · doi:10.1080/01431161.2025.2589946
“With the rise of deep learning, convolutional neural networks (CNNs) (Krizhevsky, Sutskever, and Hinton 2017) have become the mainstream approach for HSI classification due to their strong capability for automatic feature learning.”
Joseph, O., Godspower, O., Olawale, A., Uyovwieyovwe, E., Gabriel, E., Avwerosuoghene, A., & Sunday, A. (2025) · International Journal of Systems Science and Applied Mathematics · doi:10.11648/j.ijssam.20251003.12
A Convolutional Neural Network Model for Mushroom Toxicity Recognition
Irvanizam, I., Subianto, M., & Jamil, M. (2025) · Infolitika Journal of Data Science · doi:10.60084/ijds.v3i2.359
Tuberculosis Lesion Classification and Detection Method Guided by Attention Mechanism
Lu, M., Xie, H., Xu, B., et al. (2025) · International Conference on Control, Automation and Information Sciences (ICCAIS) · doi:10.1109/iccais67508.2025.11262101
“Since AlexNet [4] achieved remarkable localization and classification results in the 2012 ImageNet competition, convolutional neural networks (CNNs) have been widely applied to medical image detection and classification systems [5].”
DeNSAINet: DDoS Attack Detection using Neuro-Symbolic AI in Softwarized Networks
Dey, S., Paul, A., Mukherjee, A., Basu, D., & Ghosh, U. (2025) · IEEE India Council International Subsections Conference (INDISCON) · doi:10.1109/indiscon66021.2025.11254933
“…shifted from ML to DL techniques, considering the large volume of traffic, and incorporated hybrid learning and ensemble approaches like Long-Short Term Memory Neural Network (LSTM) [5], Recurrent Neural Network [6], Convolutional Neural Network and Gated Recurrent Unit Neural Network [7], [8].”
Reddy, G. S., Noorullah, R., Suthar, P., et al. (2025) · IEEE India Council International Subsections Conference (INDISCON) · doi:10.1109/indiscon66021.2025.11252103
“For instance, the effort of Krizhevsky et al. in ImageNet classification established the capability of CNN in extracting hierarchical features of images that formed the basis of their application in sign language recognition [6].”
Kapoor, S., & Maheshwary, P. (2025) · doi:10.1109/mpcon66082.2025.11256574
“[23,24] The Swin Transformer utilizes self-consideration instruments utilizing standard and moved window dividing setups to further develop computational productivity contrasted with ordinary Transformer models.”
Tomato Classification Using EfficientNet-B7
Barman, D., & Parashar, A. (2025) · doi:10.1109/mpcon66082.2025.11256563
Digital Coercion? The Financial Market and the Right to Digital Opt-Out between Fiction and Reality
Nieborak, T. (2025) · Białostockie Studia Prawnicze · doi:10.15290/bsp.2025.30.04.08
“…can certainly be argued that it began as early as the 1950s with the publication of Alan Turing’s essay ‘Computing machinery and intelligence’, but it was only in 2012 that the introduction of the AlexNet model demonstrated the power and potential of the new technologies (Krizhevsky et al., 2017).”
HDCA: Heterogeneous Dual-Path Contrastive Architecture for Action Recognition
Kang, S., Huo, H., Ma, L., Wang, J., & Mei, A. (2025) · Electronics · doi:10.3390/electronics14234730
Rambach, T., Gleim, P., Heizmann, C., Kellmeyer, P., & Kunze, C. (2025) · doi:10.2196/preprints.88368
Zhang, X., Ma, J., Liu, K., et al. (2025) · doi:10.1109/embc58623.2025.11252818
“The second experiment assesses the effectiveness of the frequency-domain feature extractor by comparing ResNet [13], AlexNet [14], and ShuffleNet [10].”
Ahmad, Z., Bao, S., & Chen, M. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11253609 · Cited by 1
“We explore different pre-trained models, such as ResNet [19], AlexNet [20], VGG [21] and InceptionV3 [22], in the comparative study.”
Kiani, P., Alirezaie, J., & Khan, N. (2025) · doi:10.1109/embc58623.2025.11252877
“AlexNet introduced deep convolutional networks to improve image classification, although it requires significant computational resources [21].”
Safety Helmet Wearing Detection Algorithm Based on YOLO11s-BEA
Cai, H., Xiang, L., Xu, K., et al. (2025) · International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) · doi:10.1109/cisp-bmei68103.2025.11259146
Sex Identification Using Computed Tomography Images of the Human Skull
Kazama, R. N., Belém, G. S., Cabral, E. L. L., Fugiwara, F. V. G., Martins, T. D. C., Kondo, P. N., Melani, R. F. H., & Driemeier, L. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11253006
“Several CNN architectures have marked milestones in image classification tasks: AlexNet [9] Inception (GoogleLeNet) [10] improved efficiency with inception modules; VGG [11] emphasized depth with small filters; and ResNet [12] introduced residual connections to enable very deep networks.”
Hassan, N. B., Farhan, B. A., Hasan, W. A., et al. (2025) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats66542.2025.11258439
Sivaraman, A. R., Chandrashekar, C., Sivaraman, A. K., Battina, M. R., Ajmi, A. I. Q. A., & Shakthi, S. (2025) · International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats66542.2025.11258221
Event-Based Dim and Small Target Detection by Spatiotemporal Feature Learning
Fan, R., Zhu, Q., Han, L., Guo, L., & Dong, Y. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3637141
“We compare our proposed HSC feature extractor with MobileNetV1 [58], Mobile-NetV2 [59], Mobile-Net-Xt [60], Ghost-Net [61], Efficient-Net [62], CSP-Net [63], Alex-Net [64], Res-Net [65], Res-NeXt [56], VGG [66], and Rep-VGG [67], while keeping other components unchanged.”
Modality-Aware Gated Attention Network for Audio-Visual Event Localization
Liu, L., Li, S., Zhu, Y., et al. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3640018
Metadata
- Authors
- Krizhevsky, A., Sutskever, I., & Hinton, G. E.
- Affiliations
- Google (United States) (US)
- University of Toronto (CA)
- Venue
- Communications of the ACM
- Published
- 2017-05-24
- Volume
- 60
- Issue
- 6
- Pages
- 84-90
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2163605009
- Cited by
- 112,155
- References
- 27
- Citations by year
- 2026: 2710 · 2025: 7710 · 2024: 10211 · 2023: 12267 · 2022: 12280 · 2021: 15497 · 2020: 14538 · 2019: 13976 · 2018: 9999 · 2017: 6661
- FWCI
- 5127.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- neural information processing systems
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- ImageNet, image classification, deep convolutional neural networks, dropout regularization, overfitting
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