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A fundamental challenge in artificial intelligence is learning useful representations of data that yield good performance on a downstream task, without overfitting to spurious input features.
The mnist database of handwritten digits
LeCun, Y · 1998
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The information bottleneck method
Tishby, N., Pereira, F. C., and Bialek, W · 2000
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2002
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Extracting relevant structures with side information
Chechik, G. and Tishby, N · 2003
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Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., and Bovik, A. C · 2003
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Neighbourhood components analysis
Goldberger, J., Hinton, G. E., Roweis, S., and Salakhutdinov, R. R · 2004
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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P · 2004
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Information bottleneck for gaussian variables
Chechik, G., Globerson, A., Tishby, N., and Weiss, Y · 2005
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Self-training avoids using spurious features under domain shift
Chen, Y., Wei, C., Kumar, A., and Ma, T · 2006
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Reducing the dimensionality of data with neural networks
Hinton, G. E. and Salakhutdinov, R. R · 2006
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Information-theoretic metric learning
Davis, J. V., Kulis, B., Jain, P., Sra, S., and Dhillon, I. S · 2007
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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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Distance metric learning for large margin nearest neighbor classification
Weinberger, K. Q. and Saul, L. K · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
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Image quality metrics: Psnr vs. ssim
Hore, A. and Ziou, D · 2010
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.-A., and Bottou, L · 2010
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Unbiased look at dataset bias
Torralba, A. and Efros, A. A · 2011
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The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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Large scale distributed deep networks
Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P., Yang, K., et al · 2012
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Robust solutions of optimization problems affected by uncertain probabilities
Ben-Tal, A., Den Hertog, D., De Waegenaere, A., Melenberg, B., and Rennen, G · 2013
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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A neural algorithm of artistic style
Gatys, L. A., Ecker, A. S., and Bethge, M · 2015
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Deep metric learning using triplet network
Hoffer, E. and Ailon, N · 2015
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Siamese neural networks for one-shot image recognition
Koch, G · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Facenet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., and Philbin, J · 2015
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Learning using privileged information: similarity control and knowledge transfer
Vapnik, V. and Izmailov, R · 2015
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Deep variational information bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K · 2016
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Ssimlayer: towards robust deep representation learning via nonlinear structural similarity
Abobakr, A., Hossny, M., and Nahavandi, S · 2019
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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A theoretical analysis of contrastive unsupervised representation learning
Arora, S., Khandeparkar, H., Khodak, M., Plevrakis, O., and Saunshi, N · 2019
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Towards shape biased unsupervised representation learning for domain generalization
Asadi, N., Hosseinzadeh, M., and Eftekhari, M · 2019
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Domain generalization by solving jigsaw puzzles
Carlucci, F. M., D’Innocente, A., Bucci, S., Caputo, B., and Tommasi, T · 2019
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P · 2016
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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2016
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Deep domain generalization with structured low-rank constraint
Ding, Z. and Fu, Y · 2017
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Conditional variance penalties and domain shift robustness
Heinze-Deml, C. and Meinshausen, N · 2017
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Unified deep supervised domain adaptation and generalization
Motiian, S., Piccirilli, M., Adjeroh, D. A., and Doretto, G · 2017
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Learning to generate images with perceptual similarity metrics
Snell, J., Ridgeway, K., Liao, R., Roads, B. D., Mozer, M. C., and Zemel, R. S · 2017
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Adversarial examples are not bugs, they are features
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A · 2019
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Learning not to learn: Training deep neural networks with biased data
Kim, B., Kim, H., Kim, K., Kim, S., and Kim, J · 2019
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The level weighted structural similarity loss: A step away from mse
Lu, Y · 2019
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Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2019
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Detecting out-of-distribution examples with in-distribution examples and gram matrices
Sastry, C. S. and Oore, S · 2019
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Infomask: Masked variational latent representation to localize chest disease
Taghanaki, S. A., Havaei, M., Berthier, T., Dutil, F., Di Jorio, L., Hamarneh, G., and Bengio, Y · 2019
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Tian, Y., Krishnan, D., and Isola, P · 2019
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Learning robust representations by projecting superficial statistics out
Wang, H., He, Z., Lipton, Z. C., and Xing, E. P · 2019
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Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., and Wichmann, F. A · 2020
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Model patching: Closing the subgroup performance gap with data augmentation
Goel, K., Gu, A., Li, Y., and Ré, C · 2020
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In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D · 2020
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Supervised contrastive learning
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., and Krishnan, D · 2020
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Extracting robust and accurate features via a robust information bottleneck
Pensia, A., Jog, V., and Loh, P.-L · 2020
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An investigation of why overparameterization exacerbates spurious correlations
Sagawa, S., Raghunathan, A., Koh, P. W., and Liang, P · 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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On mutual information in contrastive learning for visual representations
Wu, M., Zhuang, C., Mosse, M., Yamins, D., and Goodman, N · 2020
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Noise or signal: The role of image backgrounds in object recognition
Xiao, K., Engstrom, L., Ilyas, A., and Madry, A · 2020
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Y-net: Multi-scale feature aggregation network with wavelet structure similarity loss function for single image dehazing
Yang, H.-H., Yang, C.-H. H., and Tsai, Y.-C. J · 2020
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