Identity mappings in deep residual networks
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The architecture of ResNet18 improves the performance of visual categorization by deepening the network and addressing the problem of a limited training dataset.
“By deepening the network and addressing the problem of a limited training dataset, the architecture of ResNet18 [30] improves the performance of visual categorization.”
From An efficient deepfake video detection using robust deep learning (2024) · cites this paper as [30]
The Results (p. 12) attribute competitive CIFAR classification results to going deeper, and the Introduction (p. 2) reports improved ImageNet results with a 200-layer ResNet. The paper does not establish these results for ResNet18 or say that its architecture addresses a limited training dataset; its discussion of small datasets concerns deeper models and pre-activation units. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe structure of ShuffleNet V2 realizes the feature re-use pattern by design and shares the similar benefit of feature re-use for high accuracy as DenseNet, but is much more efficient.
“Thus, the structure of ShuffleNet V2 realizes this type of feature re-use pattern by design . It shares the similar benefit of feature re-use for high accuracy as in DenseNet [6], but it is much more efficient as analyzed earlier.”
From ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design (2018) · cites this paper as [6]
The citation is attached to the comparison with DenseNet, not to the citing authors’ analysis of ShuffleNet V2’s efficiency. The Introduction (p. 1) discusses information propagation through ResNet identity shortcuts, but our copy does not address DenseNet’s feature reuse or its accuracy benefit; searches for DenseNet and feature reuse found no supporting passage. (LLM, conf=0.76)
Experimental — accuracy not yet measuredIn each ShuffleNet V2 block, half of the feature channels directly go through the block and join the next block, which can be regarded as feature reuse.
“Second, in each block, half of feature channels (when c ′ = c/ 2) directly go through the block and join the next block. This can be regarded as a kind of feature reuse , in a similar spirit as in DenseNet [6] and CondenseNet [16].”
From ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design (2018) · cites this paper as [6]
The Introduction (p. 1) and Analysis of Deep Residual Networks (p. 2) describe identity shortcuts that let information pass directly between ResNet units. Searches for ShuffleNet V2, half-channel bypasses, and feature reuse found no passage addressing the claimed ShuffleNet V2 block design. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280 · see citing works
AASIST: Audio Anti-Spoofing Using Integrated Spectro-Temporal Graph Attention Networks
Jung, J.-W., Heo, H.-S., Tak, H., Shim, H.-J., Chung, J. S., Lee, B.-J., Yu, H.-J., & Evans, N. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9747766 · Cited by 399 · see citing works
Learning to Quantize Deep Networks by Optimizing Quantization Intervals With Task Loss
Jung, S., Son, C., Lee, S., Son, J., Han, J.-J., Kwak, Y., Hwang, S. J., & Choi, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00448 · Cited by 385 · see citing works
Zhao, M., Kang, M., Tang, B., & Pecht, M. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2762639 · Cited by 380 · see citing works
Viewpoint: Human-in-the-loop Artificial Intelligence
Zanzotto, F. M. (2019) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11345 · Cited by 380 · see citing works
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network
Zhang, Z., Xie, Y., Xing, F., McGough, M., & Yang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.378 · Cited by 354 · see citing works
GeniePath: Graph Neural Networks with Adaptive Receptive Paths
Liu, Z., Chen, C., Li, L., Zhou, J., Li, X., Song, L., & Qi, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014424 · Cited by 350 · see citing works
Combining Residual Networks with LSTMs for Lipreading
Stafylakis, T., & Tzimiropoulos, G. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-85 · Cited by 348 · see citing works
Interleaved Group Convolutions
Zhang, T., Qi, G.-J., Xiao, B., & Wang, J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.469 · Cited by 323 · see citing works
DeepMedic for Brain Tumor Segmentation
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Rethinking Bottleneck Structure for Efficient Mobile Network Design
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Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration
He, Y., Ding, Y., Liu, P., Zhu, L., Zhang, H., & Yang, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00208 · Cited by 262 · see citing works
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
Azizi, S., Culp, L., Freyberg, J., Mustafa, B., Baur, S., Kornblith, S., Chen, T., Tomasev, N., Mitrović, J., Strachan, P., et al. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01049-7 · Cited by 250 · see citing works
Development of convolutional neural network and its application in image classification: a survey
Wang, W., Yang, Y., Wang, X., Wang, W., & Li, J. (2019) · Optical Engineering · doi:10.1117/1.oe.58.4.040901 · Cited by 252 · see citing works
A2D2: Audi Autonomous Driving Dataset
Geyer, J., Kassahun, Y., Mahmudi, M., Ricou, X., Durgesh, R., Chung, A. S., Hauswald, L., Pham, V. H., Mühlegg, M., Dorn, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2004.06320 · Cited by 250 · see citing works
CrypTFlow: Secure TensorFlow Inference
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NAS-FCOS: Fast Neural Architecture Search for Object Detection
Wang, N., Gao, Y., Chen, H., Wang, P., Tian, Z., Shen, C., & Zhang, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01196 · Cited by 258 · see citing works
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ma, N., Zhang, X., Zheng, H.-T., & Sun, J. (2018) · arXiv · Cited by 7,344 · see citing works
Self-citationLSQ+: Improving low-bit quantization through learnable offsets and better initialization
Bhalgat, Y., Lee, J., Nagel, M., Blankevoort, T., & Kwak, N. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00356 · Cited by 236 · see citing works
Self-supervised model adaptation for multimodal semantic segmentation
Valada, A., Mohan, R., & Burgard, W. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01188-y · Cited by 293 · see citing works
Prostate cancer detection using deep convolutional neural networks
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Analyzing and Increasing the Reliability of Convolutional Neural Networks on GPUs
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Learning representations for the early detection of sepsis with deep neural networks
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Vision Transformers Are Robust Learners
Paul, S., & Chen, P.-Y. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20103 · Cited by 213 · see citing works
“In this work, our baseline is a ResNet50V2 model [30] pre-trained on the ImageNet-1k dataset [31] except for a few results where we consider ResNet-50 [26] 2 .”
MethodMattson, P., Cheng, C., Coleman, C., Diamos, G., Micikevicius, P., Patterson, D., Tang, H., Wei, G.-Y., Bailis, P., Bittorf, V., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.01500 · Cited by 206 · see citing works
“ResNet-50 is a residual network (He et al., 2016a;b); such networks and their derivatives remain the state of the art in image classification, and system studies commonly use them (Goyal et al., 2017; Jia et al., 2018; Mikami et al., 2018; Ying et al., 2018; Sun et al., 2019).”
BackgroundAdversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
Chen, T., Liu, S., Chang, S., Cheng, Y., Amini, L., & Wang, Z. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00078 · Cited by 205 · see citing works
How Do Vision Transformers Work?
Park, N., & Kim, S. (2022) · arXiv · doi:10.48550/arxiv.2202.06709 · Cited by 356 · see citing works
“Figure 11 shows AlterNet based on pre-activation ResNet-50 (He et al., 2016b) for CIFAR-100 as an example.”
MethodStabilizing Transformers for Reinforcement Learning
Parisotto, E., Song, H. F., Rae, J. W., Pascanu, R., Gulcehre, C., Jayakumar, S. M., Jaderberg, M., Kaufman, R. L., Clark, A., Noury, S., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.06764 · Cited by 199 · see citing works
Dual-force convolutional neural networks for accurate brain tumor segmentation
Chen, S., Ding, C., & Liu, M. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2018.11.009 · Cited by 195 · see citing works
Leichtmann, B., Humer, C., Hinterreiter, A., Streit, M., & Mara, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107539 · Cited by 200 · see citing works
CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding
Lanusse, F., Ma, Q., Li, N., Collett, T. E., Li, C.-L., Ravanbakhsh, S., Mandelbaum, R., & Póczos, B. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx1665 · Cited by 191 · see citing works
- Authors
- He, K., Zhang, X., Ren, S., & Sun, J.
- Affiliations
- Microsoft Research Asia (China) (CN)
- Corresponding author
- He, Kaiming
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 630-645
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2302255633
- Cited by
- 10,741
- References
- 6
- Citations by year
- 2026: 466 · 2025: 909 · 2024: 1286 · 2023: 1317 · 2022: 1365 · 2021: 1599 · 2020: 1446 · 2019: 1178 · 2018: 732 · 2017: 366 · 2016: 72 · 2015: 4
- FWCI
- 266.27
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- ResNet, identity mapping, skip connections, residual blocks, signal propagation, generalization, training stability
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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