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Recently, vision transformers and MLP-based models have been developed in order to address some of the prevalent weaknesses in convolutional neural networks.
Brochu, F · 1907
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Exploring the origins and prevalence of texture bias in convolutional neural networks
Hermann, K. L. and Kornblith, S · 1911
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Going deeper with convolutions, 2014
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2014
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Deep residual learning for image recognition, 2015
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Very deep convolutional networks for large-scale image recognition, 2015
Simonyan, K. and Zisserman, A · 2015
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Cognitive psychology for deep neural networks: A shape bias case study
Ritter, S., Barrett, D. G., Santoro, A., and Botvinick, M. M · 2017
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Learning inductive biases with simple neural networks
Feinman, R. and Lake, B. M · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T. G · 2018
Cited alongside, same era.
Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
Cited alongside, same era.
Pytorch image models
Wightman, R · 2019
Cited alongside, same era.
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., Uszkoreit, J., and Houlsby, N · 2021
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Natural adversarial examples, 2021
Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., and Song, D · 2021
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Shape-texture debiased neural network training
Li, Y., Yu, Q., Tan, M., Mei, J., Tang, P., Shen, W., Yuille, A., and cihang xie · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows, 2021
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B · 2021
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Does enhanced shape bias improve neural network robustness to common corruptions?
Mummadi, C. K., Subramaniam, R., Hutmacher, R., Vitay, J., Fischer, V., and Metzen, J. H · 2021
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Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., Song, D., Steinhardt, J., and Gilmer, J · 2020
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Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and Jégou, H · 2020
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Understanding robustness of transformers for image classification, 2021
Bhojanapalli, S., Chakrabarti, A., Glasner, D., Li, D., Unterthiner, T., and Veit, A · 2021
Cited alongside, same era.
Tolstikhin, I., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Keysers, D., Uszkoreit, J., Lucic, M., et al · 2021
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Going deeper with image transformers, 2021
Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., and Jégou, H · 2021
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