The DOI matches its Crossref record; 73 of the 78 references it cites were found in a registry or an index.
AutoFormer: Searching Transformers for Visual Recognition
Open accessAbstract
Recently, pure transformer-based models have shown great potentials for vision tasks such as image classification and detection. However, the design of transformer networks is challenging. It has been observed that the depth, embedding dimension, and number of heads can largely affect the performance of vision transformers. Previous models configure these dimensions based upon manual crafting. In this work, we propose a new one-shot architecture search framework, namely AutoFormer, dedicated to vision transformer search. AutoFormer entangles the weights of different blocks in the same layers during supernet training. Benefiting from the strategy, the trained supernet allows thousands of subnets to be very well-trained. Specifically, the performance of these subnets with weights inherited from the supernet is comparable to those retrained from scratch. Besides, the searched models, which we refer to AutoFormers, surpass the recent state-of-the-arts such as ViT and DeiT. In particular, AutoFormer-tiny/small/base achieve 74.7%/81.7%/82.4% top-1 accuracy on ImageNet with 5.7M/22.9M/53.7M parameters, respectively. Lastly, we verify the transferability of AutoFormer by providing the performance on downstream benchmarks and distillation experiments. Code and models are available at https://github.com/microsoft/Cream.
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“The methods include pruning [40, 41], Quantization [42-44], Knowledge Distillation [45] and Neural Architecture Search [46].”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [46]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 47,472
(2014) · Cited by 33,419
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072
Layer NormalizationPreprint
Ba, J., Kiros, J., & Hinton, G. E. (2016) · arXiv.org · doi:10.48550/arxiv.1607.06450 · Cited by 13,762
Aggregated Residual Transformations for Deep Neural Networks
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.634 · Cited by 12,465
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596
Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., Pang, R., et al. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00140 · Cited by 9,708
Learning Transferable Architectures for Scalable Image Recognition
Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00907 · Cited by 6,875
mixup: Beyond Empirical Risk Minimization
Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2017) · arXiv · doi:10.48550/arxiv.1710.09412 · Cited by 5,831
Neural Architecture Search with Reinforcement LearningPreprint
Zoph, B., & Le, Q. V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01578 · Cited by 5,705
CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features
Yun, S., Han, D., Chun, S., Oh, S. J., Yoo, Y., & Choe, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00612 · Cited by 5,400
Han (2023) · doi:10.1109/tpami.2022.3152247 · Cited by 4,682
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019) · arXiv · doi:10.48550/arxiv.1909.11942 · Cited by 4,308
Self-citationTransformers in Vision: A Survey
Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., & Shah, M. (2022) · ACM Computing Surveys · doi:10.1145/3505244 · Cited by 3,595
Gaussian Error Linear Units (GELUs)
Hendrycks, D., & Gimpel, K. (2016) · arXiv · doi:10.48550/arxiv.1606.08415 · Cited by 3,537
Automated Flower Classification over a Large Number of Classes
Nilsback, M.-E., & Zisserman, A. (2008) · 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing · doi:10.1109/icvgip.2008.47 · Cited by 3,442
MnasNet: Platform-Aware Neural Architecture Search for Mobile
Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., & Le, Q. V. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00293 · Cited by 3,416
Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E. D., Zoph, B., Shlens, J., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00359 · Cited by 3,223
CCNet: Criss-Cross Attention for Semantic Segmentation
Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., & Liu, W. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00069 · Cited by 3,045
CvT: Introducing Convolutions to Vision Transformers
Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., & Zhang, L. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00009 · Cited by 2,795
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z., Tay, F. E. H., Feng, J., & Yan, S. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00060 · Cited by 2,415
Deep Networks with Stochastic Depth
Huang, G., Sun, Y., Liu, Z., Sedra, D., & Weinberger, K. Q. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_39 · Cited by 2,104
Designing Network Design Spaces
Radosavovic, I., Kosaraju, R. P., Girshick, R., He, K., & Dollar, P. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01044 · Cited by 2,084
(2013) · Cited by 2,056
What Does BERT Look at? An Analysis of BERT’s Attention
Clark, K., Khandelwal, U., Levy, O., & Manning, C. D. (2019) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w19-4828 · Cited by 1,953
Neural Architecture Search: A Survey
Elsken, T., Metzen, J. H., & Hutter, F. (2018) · arXiv · doi:10.48550/arxiv.1808.05377 · Cited by 1,763
Parkhi, O., Vedaldi, A., Zisserman, A., & Jawahar, C. V. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248092 · Cited by 1,702
Deeply-Supervised NetsPreprint
Lee, C., Xie, S., Gallagher, P., Zhang, Z., & Tu, Z. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.5185 · Cited by 1,693
(2018) · International Conference on Machine Learning · Cited by 1,465
Bottleneck Transformers for Visual Recognition
Srinivas, A., Lin, T.-Y., Parmar, N., Shlens, J., Abbeel, P., & Vaswani, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01625 · Cited by 1,385
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
Wu, B., Keutzer, K., Dai, X., Zhang, P., Wang, Y., Sun, F., Wu, Y., Tian, Y., Vajda, P., & Jia, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01099 · Cited by 1,349
Visualizing the Loss Landscape of Neural Nets
Li, H., Xu, Z., Taylor, G., Studer, C., & Goldstein, T. (2017) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000461393 · Cited by 1,148
Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned
Voita, E., Talbot, D., Moiseev, F., Sennrich, R., & Titov, I. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1580 · Cited by 1,146
Exploring Self-Attention for Image Recognition
Zhao, H., Jia, J., & Koltun, V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01009 · Cited by 1,002
Stand-Alone Self-Attention in Vision Models
Ramachandran, P., Parmar, N., Vaswani, A., Bello, I., Levskaya, A., & Shlens, J. (2019) · arXiv · doi:10.48550/arxiv.1906.05909 · Cited by 977
Xie, L., & Yuille, A. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.154 · Cited by 919
A Convergence Theory for Deep Learning via Over-ParameterizationPreprint
Allen-Zhu, Z., Li, Y., & Song, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.03962 · Cited by 900
Single Path One-Shot Neural Architecture Search with Uniform Sampling
Guo, Z., Zhang, X., Mu, H., Heng, W., Liu, Z., Wei, Y., & Sun, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58517-4_32 · Cited by 882
Once-for-All: Train One Network and Specialize it for Efficient Deployment
Cai, H., Gan, C., Wang, T., Zhang, Z., & Han, S. (2019) · arXiv · doi:10.48550/arxiv.1908.09791 · Cited by 845
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., & Chen, L.-C. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58548-8_7 · Cited by 809
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Wang, Q., Li, B., Xiao, T., Zhu, J., Li, C., Wong, D. F., & Chao, L. S. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1176 · Cited by 673
Random Search and Reproducibility for Neural Architecture SearchPreprint
Li, L., & Talwalkar, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07638 · Cited by 582
Understanding and Simplifying One-Shot Architecture Search
Bender, G., Kindermans, P.-J., Zoph, B., Vasudevan, V., & Le, Q. V. (2018) · International Conference on Machine Learning · Cited by 558
Slimmable Neural NetworksPreprint
Yu, J., Yang, L., Xu, N., Yang, J., & Huang, T. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.08928 · Cited by 508
SMASH: One-Shot Model Architecture Search through HyperNetworksPreprint
Brock, A., Lim, T., Ritchie, J. M., & Weston, N. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.05344 · Cited by 500
Gradient Descent Provably Optimizes Over-parameterized Neural NetworksPreprint
Du, S. S., Zhai, X., Póczos, B., & Singh, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.02054 · Cited by 490
Co-Scale Conv-Attentional Image Transformers
Xu, W., Xu, Y., Chang, T. A., & Tu, Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00983 · Cited by 455
Are Sixteen Heads Really Better than One?
Michel, P., Levy, O., & Neubig, G. (2019) · arXiv · doi:10.48550/arxiv.1905.10650 · Cited by 404
Universally Slimmable Networks and Improved Training Techniques
Yu, J., & Huang, T. S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00189 · Cited by 384
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search
Chu, X., Zhang, B., & Xu, R. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01202 · Cited by 380
Regularized Evolution for Image Classifier Architecture Search
Real, E., Aggarwal, A., Huang, Y., & Le, Q. V. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014780 · Cited by 328
So, D. R., Liang, C., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1901.11117 · Cited by 277
BigNAS: Scaling up Neural Architecture Search with Big Single-Stage Models
Yu, J., Jin, P., Liu, H., Bender, G., Kindermans, P.-J., Tan, M., Huang, T. S., Song, X., Pang, R., & Le, Q. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58571-6_41 · Cited by 255
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
Wang, H., Wu, Z., Liu, Z., Cai, H., Zhu, L., Gan, C., & Han, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.686 · Cited by 227
Block-Wisely Supervised Neural Architecture Search With Knowledge Distillation
Li, C., Peng, J., Yuan, L., Wang, G., Liang, X., Lin, L., & Chang, X. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00206 · Cited by 191
PolyTransform: Deep Polygon Transformer for Instance Segmentation
Liang, J., Homayounfar, N., Ma, W.-C., Xiong, Y., Hu, R., & Urtasun, R. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00915 · Cited by 188
Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., & Jégou, H. (2020) · arXiv · doi:10.48550/arxiv.2012.12877 · Cited by 182
Training Deeper Neural Machine Translation Models with Transparent Attention
Bapna, A., Chen, M., Fırat, O., Cao, Y. J., & Wu, Y. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1338 · Cited by 144
Self-citationLi, C., Tang, T., Wang, G., Peng, J., Wang, B., Liang, X., & Chang, X. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01206 · Cited by 111
Collecting a Large-scale Dataset of Fine-grained Cars
Krause, J., Deng, J., Stark, M. M., & Fei-Fei, L. (2013) · MPG.PuRe (Max Planck Society) · Cited by 100
Grafit: Learning fine-grained image representations with coarse labels
Touvron, H., Sablayrolles, A., Douze, M., Cord, M., & Jeǵou, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00091 · Cited by 77
Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture SearchPreprint
Peng, H., Du, H., Yu, H., Li, Q., Liao, J., & Fu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.15821 · Cited by 57
Self-citationAugment your batch: better training with larger batchesPreprint
Hoffer, E., Ben‐Nun, T., Hubara, I., Giladi, N., Hoefler, T., & Soudry, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.09335 · Cited by 45
Go Wide, Then Narrow: Efficient Training of Deep Thin NetworksPreprint
Zhou, D., Ye, M., Chen, C., Meng, T., Tan, M., Song, X., Le, Q. V., Liu, Q., & Schuurmans, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00811 · Cited by 13
Research works printed but not linked: 2
- li, 2020, Blockwisely supervised neural architecture search with knowledge distillation, CVPR
- huang, 2019, Ccnet: Crisscross attention for semantic segmentation, ICCV
Cited by
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Transformers in Vision: A Survey
Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., & Shah, M. (2022) · ACM Computing Surveys · doi:10.1145/3505244 · Cited by 3,595
SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer
Ma, J., Tang, L., Fan, F., Huang, J., Mei, X., & Ma, Y. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105686 · Cited by 1,557
Transformers in Time Series: A Survey
Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., & Sun, L. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/759 · Cited by 1,260
EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention
Liu, X., Peng, H., Zheng, N., Yang, Y., Hu, H., & Yuan, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01386 · Cited by 956
Self-citation
Metadata
- Authors
- Chen, M., Peng, H., Fu, J., & Ling, H.
- Affiliations
- Stony Brook University (US)
- Microsoft Research Asia (China) (CN)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 12250-12260
- OpenAlex
- W3204801262
- Cited by
- 323
- References
- 78
- Citations by year
- 2026: 28 · 2025: 75 · 2024: 103 · 2023: 79 · 2022: 33 · 2021: 5
- FWCI
- 17.99
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (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 › Machine Learning and Data Classification
- Keywords
- vision transformer, image classification, transferability, knowledge distillation, Autoformer
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