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Invariance to a broad array of image corruptions, such as warping, noise, or color shifts, is an important aspect of building robust models in computer vision.
Transformation invariance in pattern recognition—tangent distance and tangent propagation
Simard, P. Y., LeCun, Y. A., Denker, J. S., and Victorri, B · 1998
Earlier work this paper cites.
JH Labs Java Image Processing, 2006
Huxtable, J · 2006
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Invariant scattering convolution networks
Bruna, J. and Mallat, S · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Earlier work this paper cites.
Filterpedia, 2016
Gladman, S. J · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
Earlier work this paper cites.
A study and comparison of human and deep learning recognition performance under visual distortions
Dodge, S. and Karam, L · 2017
Earlier work this paper cites.
Accurate, large minibatch SGD: Training ImageNet in 1 hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K · 2017
Earlier work this paper cites.
Generalisation in humans and deep neural networks
Geirhos, R., Temme, C. R., Rauber, J., Schütt, H. H., Bethge, M., and Wichmann, F. A · 2018
Earlier work this paper cites.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2018
Earlier work this paper cites.
Exploring the limits of weakly supervised pretraining
Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., and van der Maaten, L · 2018
Earlier work this paper cites.
Do CIFAR-10 classifiers generalize to CIFAR-10?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2018
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AutoAugment: Learning augmentation strategies from data
Cubuk, E. D., Zoph, B., Mané, D., Vasudevan, V., and Le, Q. V · 2019
Cited alongside, same era.
A kernel theory of modern data augmentation
Dao, T., Gu, A., Ratner, A. J., Smith, V., De Sa, C., and Ré, C · 2019
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.
AugMix: A simple data processing method to improve robustness and uncertainty
Hendrycks, D., Mu, N., Cubuk, E. D., Zoph, B., Gilmer, J., and Lakshminarayanan, B · 2019
Cited alongside, same era.
The many faces of robustness: A critical analysis of out-of-distribution generalization
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
Later among the works it cites.
Compounding the performance improvements of assembled techniques in a convolutional neural network
Lee, J., Won, T., and Hong, K · 2020
Later among the works it cites.
Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Liang, J., Hu, D., and Feng, J · 2020
Later among the works it cites.
Designing network design spaces
Radosavovic, I., Kosaraju, R. P., Girshick, R., He, K., and Dollár, P · 2020
Later among the works it cites.
A simple way to make neural networks robust against diverse image corruptions
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Lopes, R. G., Yin, D., Poole, B., Gilmer, J., and Cubuk, E. D · 2019
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Robustness properties of Facebook’s ResNeXt WSL models
Orhan, A. E · 2019
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On network design spaces for visual recognition
Radosavovic, I., Johnson, J., Xie, S., Lo, W.-Y., and Dollár, P · 2019
Cited alongside, same era.
Do ImageNet classifiers generalize to ImageNet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2019
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Do image classifiers generalize across time?
Shankar, V., Dave, A., Roelofs, R., Ramanan, D., Recht, B., and Schmidt, L · 2019
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EfficientNet: Rethinking model scaling for convolutional neural networks
Tan, M. and Le, Q · 2019
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Hydra - a framework for elegantly configuring complex applications
Yadan, O · 2019
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Rusak, E., Schott, L., Zimmermann, R., Bitterwolf, J., Bringmann, O., Bethge, M., and Brendel, W · 2020
Later among the works it cites.
Improving robustness against common corruptions by covariate shift adaptation
Schneider, S., Rusak, E., Eck, L., Bringmann, O., Brendel, W., and Bethge, M · 2020
Later among the works it cites.
Test-time training with self-supervision for generalization under distribution shifts
Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A., and Hardt, M · 2020
Later among the works it cites.
Measuring robustness to natural distribution shifts in image classification
Taori, R., Dave, A., Shankar, V., Carlini, N., Recht, B., and Schmidt, L · 2020
Later among the works it cites.
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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On the generalization effects of linear transformations in data augmentation
Wu, S., Zhang, H. R., Valiant, G., and Ré, C · 2020
Later among the works it cites.
Self-training with Noisy Student improves imagenet classification
Xie, Q., Luong, M.-T., Hovy, E., and Le, Q. V · 2020
Later among the works it cites.
ResNeSt: Split-Attention Networks
Zhang, H., Wu, C., Zhang, Z., Zhu, Y., Zhang, Z., Lin, H., Sun, Y., He, T., Muller, J., Manmatha, R., Li, M., and Smola, A · 2020
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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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Tent: Fully test-time adaptation by entropy minimization
Wang, D., Shelhamer, E., Liu, S., Olshausen, B., and Darrell, T · 2021
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