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We introduce InstaAug, a method for automatically learning input-specific augmentations from data.
Invariance reduces variance: Understanding data augmentation in deep learning and beyond
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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Kallenberg, O. and Kallenberg, O · 1997
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Spatial transformer networks
Jaderberg, M., Simonyan, K., Zisserman, A., et al · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Group equivariant convolutional networks
Cohen, T. and Welling, M · 2016
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Deep learning
Goodfellow, I., Bengio, Y., and Courville, A · 2016
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Improving cnn-based texture classification by color balancing
Bianco, S., Cusano, C., Napoletano, P., and Schettini, R · 2017
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Rotation equivariant vector field networks
Marcos, D., Volpi, M., Komodakis, N., and Tuia, D · 2017
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The effectiveness of data augmentation in image classification using deep learning
Perez, L. and Wang, J · 2017
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Harmonic networks: Deep translation and rotation equivariance
Worrall, D. E., Garbin, S. J., Turmukhambetov, D., and Brostow, G. J · 2017
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Oriented response networks
Zhou, Y., Ye, Q., Qiu, Q., and Jiao, J · 2017
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Autoaugment: Learning augmentation policies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2018
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Data augmentation for improving deep learning in image classification problem
Mikołajczyk, A. and Grochowski, M · 2018
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2018
Cited alongside, same era.
Learning representations by maximizing mutual information across views
Bachman, P., Hjelm, R. D., and Buchwalter, W · 2019
Cited alongside, same era.
Population based augmentation: Efficient learning of augmentation policy schedules
Ho, D., Liang, E., Chen, X., Stoica, I., and Abbeel, P · 2019
Cited alongside, same era.
Fast autoaugment
Lim, S., Kim, I., Kim, T., Kim, C., and Kim, S · 2019
Cited alongside, same era.
A survey on image data augmentation for deep learning
Shorten, C. and Khoshgoftaar, T. M · 2019
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Scale-equivariant steerable networks
Sosnovik, I., Szmaja, M., and Smeulders, A · 2019
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On the benefits of invariance in neural networks
Lyle, C., van der Wilk, M., Kwiatkowska, M., Gal, Y., and Bloem-Reddy, B · 2020
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Viewmaker networks: Learning views for unsupervised representation learning
Tamkin, A., Wu, M., and Goodman, N · 2020
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What makes for good views for contrastive learning?
Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., and Isola, P · 2020
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Meta-learning symmetries by reparameterization
Zhou, A., Knowles, T., and Finn, C · 2020
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Xcit: Cross-covariance image transformers
Ali, A., Touvron, H., Caron, M., Bojanowski, P., Douze, M., Joulin, A., Laptev, I., Neverova, N., Synnaeve, G., Verbeek, J., et al · 2021
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Adatransform: Adaptive data transformation
Tang, Z., Peng, X., Li, T., Zhu, Y., and Metaxas, D. N · 2019
Cited alongside, same era.
Pytorch image models
Wightman, R · 2019
Cited alongside, same era.
Deep scale-spaces: Equivariance over scale
Worrall, D. and Welling, M · 2019
Cited alongside, same era.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y · 2019
Cited alongside, same era.
Making convolutional networks shift-invariant again
Zhang, R · 2019
Cited alongside, same era.
Learning invariances in neural networks
Benton, G., Finzi, M., Izmailov, P., and Wilson, A. G · 2020
Cited alongside, same era.
Truly shift-invariant convolutional neural networks
Chaman, A. and Dokmanic, I · 2021
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Robust and accurate object detection via adversarial learning
Chen, X., Xie, C., Tan, M., Zhang, L., Hsieh, C.-J., and Gong, B · 2021
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Whitening for self-supervised representation learning
Ermolov, A., Siarohin, A., Sangineto, E., and Sebe, N · 2021
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Improving transformation invariance in contrastive representation learning
Foster, A., Pukdee, R., and Rainforth, T · 2021
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Mixmo: Mixing multiple inputs for multiple outputs via deep subnetworks
Ramé, A., Sun, R., and Cord, M · 2021
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Better aggregation in test-time augmentation
Shanmugam, D., Blalock, D., Balakrishnan, G., and Guttag, J · 2021
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Group equivariant subsampling
Xu, J., Kim, H., Rainforth, T., and Teh, Y · 2021
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Metaaugment: Sample-aware data augmentation policy learning
Zhou, F., Li, J., Xie, C., Chen, F., Hong, L., Sun, R., and Li, Z · 2021
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Adaaug: Learning class- and instance-adaptive data augmentation policies
Cheung, T.-H. and Yeung, D.-Y · 2022
Closest in time.
Saliency grafting: Innocuous attribution-guided mixup with calibrated label mixing
Park, J., Yang, J. Y., Shin, J., Hwang, S. J., and Yang, E · 2022
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Un-mix: Rethinking image mixtures for unsupervised visual representation learning
Shen, Z., Liu, Z., Liu, Z., Savvides, M., Darrell, T., and Xing, E · 2022
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Deep autoaugment
Zheng, Y., Zhang, Z., Yan, S., and Zhang, M · 2022
Closest in time.