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.
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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.
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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.
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- 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.
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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.
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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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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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Cited by
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Improved Brain Tumor Classification Using Capsule Networks with SAMME.R Boosting
Sandhya, G., Babu, B. J., Prameela, K. S., Vani, M., Sree, T. N., & Tejaswini, P. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-22118-6_12
Rolling Bearing Fault Diagnosis Method Based on RSBU-MSCNN under Strong Background Noise
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Guo, Y., Gao, W., Yuan, H., Li, W., & Chen, T. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134433
Campanharo, C. V., Uchiya, T. D. S., Campanharo, B. P., Alvarenga, F. D. S., Giacinti, G. M., Pavan, I. P., Caetano, S. S. S., Caetano, S. S. S., Casotti, M. C., Altoé, L. S. C., et al. (2026) · RECIMA21 - Revista Científica Multidisciplinar · doi:10.47820/recima21.v7i7.8430
“Since then, the convolutional neural network model has been successful in studies in different medical areas that work directly with images, making it important, therefore, to understand the bases behind these algorithms (Krizhevsky et al. , 2017).”
A Study of Denoising Method of GPR Data Based on Denoising Convolutional Neural Network
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Comparative Analysis of Deep Learning Techniques for Breast Tumor Segmentation in DW-MRI Images
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Nazari, M. H., & Bang, J. (2026) · Energies · doi:10.3390/en19133184
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“Activation layers, such as the rectified linear unit (ReLU), are subsequently applied to introduce nonlinearity into the network, enabling the model to learn more complex patterns and representations [29].”
Li, X., Mu, L., & Wang, Z. (2026) · Journal of Food Measurement & Characterization · doi:10.1007/s11694-026-04617-y
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Particle identification algorithm of the RICH detector on STCF using CNN method
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“To further accelerate training, a transfer learning strategy is adopted, and the model is initialized with parameters pretrained on the ImageNet dataset [33].”
Liu, H., Pan, X., Yu, W. K., Gao, S., Zhang, Y., & Zhang, C. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3710291
Buva, K. B., Deshmukh, A. A., Shete, M. V., Shete, A. V., & Gangurde, P. V. (2026) · Evidence-Based Dentistry · doi:10.1038/s41432-026-01226-y
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Xu, T., & Liu, C. (2026) · Scientific Reports · doi:10.1038/s41598-026-59328-7
“The CNN branch passes financial-indicator matrices through three convolutional blocks, each with filters that catch patterns at different scales 31 .”
Neural-network-assisted polarization-robust FBG interrogation based on an integrated SOI-AWG
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Zha, J., Teng, A., Shan, X., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10176856/v1
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Dolbachian, L., Alaya, M. Z., Harizi, W., Gnaba, I., Aboura, Z., & Bouzebda, S. (2026) · Mechanics of Advanced Materials and Structures · doi:10.1080/15376494.2026.2695259
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“…and less abstract than those in the prior layer, supported by the predicted output being based on known neurobiological mechanisms or structures [20]; and in the proposed multi-layer SNN the corresponding sequence would be that population-level spike selectivity at each successive layer of the…”
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“Two stage methods, such as CNN [3], R-CNN, and Faster R-CNN[4], first generate candidate regions and then perform classification and regression.”
Explainable Disentangled GAN for Controllable Brain Tumor MRI Synthesis and Counterfactual Analysis
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“Krizhevsky et al. [19] demonstrated the effectiveness of deep convolutional neural networks (CNNs) for image classification through AlexNet, while He et al. [9] introduced Residual Networks (ResNet), enabling deeper and more efficient neural architectures.”
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“Deep learning gained widespread prominence following the breakthrough work of Krizhevsky et al. [1], who demonstrated that deep CNNs could achieve state-of-the-art large-scale image classification on the ImageNet dataset, surpassing earlier machine learning methods and establishing CNNs as the…”
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“Convolutional neural networks (CNNs) have been developed, introducing automated layered feature learning, which has greatly enhanced underwater classification and detection performance [8].”
Lightweight Research on Fabric Defect Detection Based on YOLOv10s
肖, 钦. (2026) · Journal of Image and Signal Processing · doi:10.12677/jisp.2026.153027
Clinical Decision Support AI Startups: Funding Disparities and Deployment Patterns
Soh, P.-H., & Lee, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30710-1_50
Godavarthi, B., & Arunmozhi, S. (2026) · International Conference on System, Computation, Automation and Networking (ICSCAN) · doi:10.1109/icscan66520.2026.11588365 · Cited by 1
“But there are several kinds of noise that may affect the quality of medical images, and are added in the process of medical image acquisition, transmission, reconstruction and post-processing storage processes [4].”
Feng, X., Xin, R., Yao, D., et al. (2026) · ACS Omega · doi:10.1021/acsomega.6c02602
“Krizhevsky et al. 26 reported that LSTM achieved an average classification accuracy of 95%, out-performing RNN’s 92.5%.”
InfluentialJiménez-Uribe, D. A., Sánchez, D. G., Jimenez, J. D., Barrios, R. L. A., Labrador, C. R., Altamar-Mercado, H., & Patiño-Vanegas, A. (2026) · Research Square · doi:10.21203/rs.3.rs-9997567/v1
A Review on the Application of Deep Learning in Wafer Defect Detection
Li, M., Su, H., Shi, C., Zhang, Y., Cheng, X., & Xie, Y. (2026) · International Annual Conference on Complex Systems and Intelligent Science (CSIS-IAC) · doi:10.1109/csis-iac70275.2026.11585140
“…Among them, W ijc represents the filter weights, X ijc represents the input pixel values, b is the bias term, and through stacking multiple convolution and pooling layers, CNN automatically learns the hierarchical representations of defect patterns from edges, textures to semantic abstraction [20].”
Machine learning in ant biology research: A systematic review
Turza, F., & Książek, W. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115901
Fundamentals of Artificial Intelligence in Drug Discovery and Healthcare
Fakhoury, N. (2026) · Springer eBooks · doi:10.1007/978-3-032-19536-4_2
Reimagining the Synthetic Biology DBTL Cycle with Machine Learning
Lee, M., Wang, Y., Rai, K., Li, S., Whited, H. K., Araujo, M. A. P., & Bashor, C. J. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5304-3_1
Cambricon-QM: A Hybrid Architecture for Microscaling Format Training
Liu, X. S., Zhao, Y., Liu, C., et al. (2026) · IEEE Transactions on Computers · doi:10.1109/tc.2026.3710209
A chemistry-informed deep learning network for mitigating the stratospheric OH data gap
Yin, W., Zhou, C., Feng, W., Dhomse, S. S., Pope, R. J., Millan, L. F., & Chipperfield, M. P. (2026) · Science Advances · doi:10.1126/sciadv.aee2319
“Our investigation indicates that this shortcut learning is too strong during model training, even when applying modern ML regularization techniques such as dropout and weight decay ( 39 , 40 ).”
CNN ‐ MVM : An Interactive Dual‐Stream Network for Meningioma Subtype Classification
He, Y., Wang, S., Lian, Y., et al. (2026) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70407
Kirov, S., Gjini, E., Parmar, C., Motley, W. W., Kakrecha, B., Drage, M. G., Glass, B., Javed, S. A., Brosnan-Cashman, J., Wapinski, I. N., et al. (2026) · AI in Precision Oncology · doi:10.1177/2993091x261463923
Geometric-semantic dual-adaptation for generalizable AI-generated image detection
Li, J. C., Minqing, Z., Huang, S., Li, X., Feng, P., & Liu, L. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134418
Abdelnaby, K. M. (2026) · Applied Sciences · doi:10.3390/app16146838
Deep Feature Learning with Concatenated Rectified Pooling Units
Siddiqui, H., Banerjee, C., Blasch, E., Pasiliao, E. L., & Mukherjee, T. (2026) · Big Data Mining and Analytics · doi:10.26599/bdma.2025.9020079
Fingerprint-Based Blood Group Prediction: An Assessment of Deep Learning Models
Sachan, R. K., Gupta, N. K., & Kumar, A. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.06.091
Demonstrating superresolution in radar range estimation using a denoising autoencoder
Anonymous, Chakraborty, A., Jordan, A. N., et al. (2026) · Physical Review Research · doi:10.1103/w6m6-n6rk
Exploring Transfer Learning in the Context of Speckle Noise Level Identification
Panwar, A., Kumain, S. C., & Singh, H. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.06.396
Almeida, C. D. P., & Buenaño, N. D. L. J. (2026) · Frontiers in Built Environment · doi:10.3389/fbuil.2026.1883353 · Cited by 2
Violence Detection Using CNN and Bi-LSTM
Bharti, R., Kumari, S., & Kumar, V. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.06.401
Contreras, B. P. M., Garcia, E. M., Lopez, C. C., & Medina, L. E. G. (2026) · Latin-American Journal of Computing · doi:10.33333/lajc.vol13n2.03
Khade, V. C., Patil, M. S., & Patil, R. J. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.06.544
CoFormer: Coupling Attentive Model for Visual Sentiment Analysis with Hierarchical Emotion Loss
Luo, G., Zhang, H., Han, X., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-22261-9_39
Chen, X., Li, L.-W., Chen, Y., et al. (2026) · Remote Sensing · doi:10.3390/rs18132264
“Deep networks represented by AlexNet, VGGNet, GoogLeNet, ResNet, and DenseNet can automatically learn hierarchical representations—ranging from edges and textures to local objects and global scene semantics—through multilayer nonlinear mappings [8–11].”
Enhancing Adversarial Transferability by Attention Center Shuffle and Mask
Liu, Q. G., Ye, F., Lu, C., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2497-5_24
Chen, C., & Li, X. (2026) · Frontiers in Future Transportation · doi:10.3389/ffutr.2026.1711068
“During the training process, U-Net employs categorical cross-entropy loss (Krizhevsky et al., 2017) to optimize the model parameters.”
Sun, M., Zhang, X., Qi, J., & Ruan, Y. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.112156
A bibliometric retrospective of Computers & Security
Khorshidi, M. S., Merigó, J. M., Beydoun, G., Susilo, W., & Spafford, E. H. (2026) · Computers & Security · doi:10.1016/j.cose.2026.105060
Evaluation of Microservices Deployments for AI-Driven IoT and Robotic Systems
Branco, D., Martino, B. D., Espósito, A., et al. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-032-30573-2_15
Weiß, T., Hoffmann, P., Garkusha, P., Vieltorf, F., & Zaeh, M. F. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-18685-3
“In general, the architecture of the LeNet [ 44 ] and AlexNet [45 ] are less deep compared to CNNs chosen for the transfer learning approach.”
Liu, Y., Jian, A., Sun, W., & Yu, G. (2026) · Journal of Computational Mathematics · doi:10.4208/jcm.2605-m2026-0004
Positive-Incentive Noise Guided Adaptive Object Tracking via Motion State Transition
Lu, Y., & Peng, H. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3711982
Discriminative transfer feature learning for unsupervised domain adaptation
Li, M., Li, Z., & Jiang, J. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109359
Pest-induced damage detection in corn leaves using improved Faster R-CNN
Timilsina, S., Sharma, S., Sato, K., Okada, Y., Watanabe, S., & Kondo, S. (2026) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2026.112158
FPW-YOLO11n: A Lightweight Frequency-Perception Framework for Lunar Impact Crater Detection
Liang, J., Yan, P., Wen, Q., Liu, Q., Zhai, Y., & Tian, X. (2026) · Sensors · doi:10.3390/s26144344
“In 2012, the convolutional neural network AlexNet achieved breakthrough performance in the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC-2012), significantly reducing the error rate in image classification [21].”
Artificial intelligence and machine learning in the cosmetics industry
Khan, M. R., Ho, P., & Chang, A. L. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41499-2.00018-5
Zhu, R., Qi, Y., Zhao, Z., et al. (2026) · Expert Systems · doi:10.1111/exsy.70344
“Due to the progress of artificial intelligence technology, target detection algorithms have achieved significant improvements in detection speed and accuracy, and many deep learning-based methods (Hinton and Osindero 2006; Vincent et al. 2010; Krizhevsky et al. 2017) have been developed.”
Sánchez, C. F. R., Cooke, M. L., Chaipornkaew, L., et al. (2026) · Journal of Geophysical Research Solid Earth · doi:10.1029/2025jb032200
“The CNN follows a convolutional neural architecture inspired by LeNet‐5 and AlexNet networks (Krizhevsky et al., 2017; LeCun et al., 2010) that use stacked convolutional layers with kernel sizes and dilation parameters that allow the detection of small‐scale features such as stepovers between…”
Jang, H., Park, C. B., Kim, J., & Kim, J. (2026) · Physical Chemistry Chemical Physics · doi:10.1039/d6cp01420h
Joo, H., & Lee, O. (2026) · Urban Science · doi:10.3390/urbansci10070398
Samarakoon, I. B., Almurib, H. A., & Shanmugam, S. A. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12240-7
el Yadari, F. Z., Chougrad, H., & Khamlichi, Y. I. (2026) · Discover Computing · doi:10.1007/s10791-026-10299-3
“It is capable of automatically extracting relevant features from medical images, which in turn makes them highly valuable for operations like classification and pathology detection [22, 23].”
inteligencia artificial, La inteligencia artificial y su impacto en la sociedad
Soler, L. C. T., & Castro, Y. S. Y. (2026) · Revista Educ ção Científica · doi:10.46616/rce.v12i18.168
Jia, Y., Tan, E. L., Ding, A., et al. (2026) · doi:10.1109/apemc65388.2026.11593695
“In recent years, deep learning has made great progress [4], [5].”
Neural Network Based Dynamic Domain Decomposition for Multiscale Kinetic Equations
Wang, Q. M., Cai, Y., & Xiong, T. (2026) · Communications in Computational Physics · doi:10.4208/cicp.oa-2025-0228
Hansert, J., Gechter, F., & Offermann, P. (2026) · International Journal of Neural Systems · doi:10.1142/s0129065727500092
Jing, X., Zhu, R., & Miao, Y. (2026) · Journal of Hydrology Regional Studies · doi:10.1016/j.ejrh.2026.103719
The Transformational Impact of AI on Employment
Wiśniewski, T., & Paździor, M. (2026) · Journal for Perspectives of Economic Political and Social Integration · doi:10.18290/pepsi-2026-0005
“…achievement of AlexNet in the ImageNet competition on Septem - ber 30, 2012, when a deep convolutional network developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton achieved a top-5 error of 15.3%, surpassing the closest competitor by 10.8 percentage points (Krizhevsky et al., 2017).”
InfluentialSolar Cell Defect Detection Based on Deep Learning Algorithms: A Review
Xu, L., Zhu, M., Yang, X., et al. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10713-5
Use of Artificial Intelligence in Biofabrication
Uysal, G., Elçin, A. E., & Elçin, Y. M. (2026) · Stem cell biology and regenerative medicine · doi:10.1007/978-3-032-30294-6_16
Fine-Grained Recognition of Sheep Faces Based on Multi-path Feature Fusion
Liu, X., Zhi, M., Q, J., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3447-9_3
Deep Learning-Based Lidar Inversion for Aerosol Extinction Profile Using Large-Scale Synthetic Data
Lee, K.-H., Noh, Y., & Kim, K. (2026) · Journal of Korean Society for Atmospheric Environment · doi:10.5572/kosae.2026.42.2.233
New Models for Art: AI as Representational Tool
Wasielewski, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-21685-4_2
Eseg: a Real-Time Human Instance Segmentation Model for Event-Based Cameras
Barry, M., & Widmer, A. (2026) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-30801-6_17
A Novel Deep Learning Model for High‐Latitude Ionospheric Convection Using SuperDARN Data
Gao, B., Liu, E., Hu, Z., et al. (2026) · Space Weather · doi:10.1029/2026sw005037
“In the field of deep learning, the depth really is important for achieving results (Krizhevsky et al., 2012).”
Transformer-CNN Fusion With Texture Features for Robust Multiclass Blood Cell Classification
Eslamifar, O., Soltani, M., & Fatemi, S. (2026) · Applied Computational Intelligence and Soft Computing · doi:10.1155/acis/4940976
Zhang, W., Han, S., Xu, X., Xu, J., Jiang, G., Sun, M., Wang, B., & Zhang, P. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3712268
Automated quantification of drug‐induced cell death via vacuolation scoring in microscopic images
Navaneeth Krishna, S., Hanson, S., Nedungadi, D., Mishra, N., & Gopakumar, G. (2026) · Journal of Microscopy · doi:10.1111/jmi.70128
Gangwar, T., Dube, A., Singh, H. N., Payra, S., Verma, S., & Prasad, V. S. (2026) · Environmental Science Advances · doi:10.1039/d6va00160b
Hybrid Explainable Learning for Early Parkinson’s Classification
Noun, A., Soubra, R., & Chkeir, A. (2026) · International Conference on Bio-engineering for Smart Technologies (BioSMART) · doi:10.1109/biosmart71257.2026.11598040
“Grayscale slices were repeated across three channels to create a pseudo-RGB representation because the CNN used in this study requires multi-channel input [15].”
Baktash, A. P., Bakirov, K., Shoman, A., et al. (2026) · IEEE International Conference on Smart Information Systems and Technologies (SIST) · doi:10.1109/sist61674.2026.11596079
“Nonetheless, a notable practical obstacle hinders implementation in vertical farming: while regulated indoor farms aggressively mitigate illness through environmental management, they generally possess few or no diseased plants from which to gather training data [6].”
Deep Learning–Assisted Ultrahigh‐Cycle Bending Fatigue of Precorroded Completion Strings
Zhu, Z., Kong, H., Chai, G., et al. (2026) · Fatigue & Fracture of Engineering Materials & Structures · doi:10.1111/ffe.70342
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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