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Self-supervised representation learning often uses data augmentations to induce some invariance to "style" attributes of the data.
Language models are few-shot learners
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Analyse des liaisons de probabilité
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Signature verification using a "siamese" time delay neural network
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Separating style and content
Tenenbaum, J. and Freeman, W. (1996) · 1996
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Nonlinear independent component analysis: Existence and uniqueness results
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Improved baselines with momentum contrastive learning
Chen, X., Fan, H., Girshick, R., and He, K. (2020b) · 2003
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The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A. (2007) · 2007
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Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A. (2008) · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. (2009) · 2009
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Causality: Models, Reasoning, and Inference
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Sun database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K., Oliva, A., and Torralba, A. (2010) · 2010
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The caltech-ucsd birds-200-2011 dataset
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Disentangling factors of variation via generative entangling
Desjardins, G., Courville, A., and Bengio, Y. (2012) · 2012
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Cats and dogs
Parkhi, O., Vedaldi, A., Zisserman, A., and Jawahar, C. V. (2012) · 2012
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Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P. (2013) · 2013
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3d object representations for fine-grained categorization
Krause, J., Stark, M., Deng, J., and Fei-Fei, L. (2013) · 2013
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Fine-grained visual classification of aircraft
Maji, S., Rahtu, E., Kannala, J., Blaschko, M., and Vedaldi, A. (2013) · 2013
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Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., , and Vedaldi, A. (2014) · 2014
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Deep convolutional inverse graphics network
Kulkarni, T. D., Whitney, W. F., Kohli, P., and Tenenbaum, J. (2015) · 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. S., Berg, A. C., and Fei-Fei, L. (2015) · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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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) · 2017
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β \beta -VAE: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A. (2017) · 2017
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SGDR: Stochastic gradient descent with restarts
Loshchilov, I. and Hutter, F. (2017) · 2017
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Large batch training of convolutional networks
You, Y., Gitman, I., and Ginsburg, B. (2017) · 2017
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Multi-level variational autoencoder: Learning disentangled representations from grouped observations
Bouchacourt, D., Tomioka, R., and Nowozin, S. (2018) · 2018
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A framework for the quantitative evaluation of disentangled representations
Eastwood, C. and Williams, C. K. I. (2018) · 2018
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B. (2021) · 2021
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Toward causal representation learning
Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., and Bengio, Y. (2021) · 2021
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Self-supervised learning with data augmentations provably isolates content from style
von Kügelgen, J., Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve, M., and Locatello, F. (2021) · 2021
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Desiderata for representation learning: A causal perspective
Wang, Y. and Jordan, M. I. (2021) · 2021
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What should not be contrastive in contrastive learning
Xiao, T., Wang, X., Efros, A. A., and Darrell, T. (2021) · 2021
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The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA
Gresele, L., Rubenstein, P. K., Mehrjou, A., Locatello, F., and Schölkopf, B. (2019) · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., and Bachem, O. (2019) · 2019
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Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness
Suter, R., Miladinovic, D., Schölkopf, B., and Bauer, S. (2019) · 2019
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Bootstrap Your Own Latent: A new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., Piot, B., Kavukcuoglu, K., Munos, R., and Valko, M. (2020) · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020) · 2020
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Self-supervised label augmentation via input transformations
Lee, H., Hwang, S. J., and Shin, J. (2020) · 2020
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Weakly-supervised disentanglement without compromises
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Barlow twins: Self-supervised learning via redundancy reduction
Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S. (2021) · 2021
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Contrastive learning inverts the data generating process
Zimmermann, R. S., Sharma, Y., Schneider, S., Bethge, M., and Brendel, W. (2021) · 2021
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Weakly supervised representation learning with sparse perturbations
Ahuja, K., Hartford, J. S., and Bengio, Y. (2022) · 2022
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VICReg: Variance-invariance-covariance regularization for self-supervised learning
Bardes, A., Ponce, J., and LeCun, Y. (2022) · 2022
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Weakly supervised causal representation learning
Brehmer, J., De Haan, P., Lippe, P., and Cohen, T. (2022) · 2022
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When does contrastive visual representation learning work?
Cole, E., Yang, X., Wilber, K., Mac Aodha, O., and Belongie, S. (2022) · 2022
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Equivariant self-supervised learning: Encouraging equivariance in representations
Dangovski, R., Jing, L., Loh, C., Han, S., Srivastava, A., Cheung, B., Agrawal, P., and Soljacic, M. (2022) · 2022
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Understanding and improving the role of projection head in self-supervised learning
Gupta, K., Ajanthan, T., Hengel, A. v. d., and Gould, S. (2022) · 2022
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Understanding dimensional collapse in contrastive self-supervised learning
Jing, L., Vincent, P., LeCun, Y., and Tian, Y. (2022) · 2022
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Caltech 101
Li, F.-F., Andreeto, M., Ranzato, M., and Perona, P. (2022) · 2022
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Guillotine regularization: Why removing layers is needed to improve generalization in self-supervised learning
Bordes, F., Balestriero, R., Garrido, Q., Bardes, A., and Vincent, P. (2023) · 2023
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Identifiability results for multimodal contrastive learning
Daunhawer, I., Bizeul, A., Palumbo, E., Marx, A., and Vogt, J. E. (2023) · 2023
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DCI-ES: An extended disentanglement framework with connections to identifiability
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On the duality between contrastive and non-contrastive self-supervised learning
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Linear causal disentanglement via interventions
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Investigating the benefits of projection head for representation learning
Xue, Y., Gan, E., Ni, J., Joshi, S., and Mirzasoleiman, B. (2024) · 2024
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Multi-view causal representation learning with partial observability
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