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Contrastive learning has recently seen tremendous success in self-supervised learning.
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Kernel-based nonlinear blind source separation
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Imagenet: A large-scale hierarchical image database
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Nonlinear mixtures
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Are we ready for autonomous driving? the KITTI vision benchmark suite
Geiger, A., Lenz, P., and Urtasun, R · 2012
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Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics
Gutmann, M. U. and Hyvärinen, A · 2012
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Smooth manifolds
Lee, J. M · 2013
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An extension of slow feature analysis for nonlinear blind source separation
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Kingma, D. P. and Ba, J · 2015
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On global inversion of homogeneous maps
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Unsupervised feature extraction by time-contrastive learning and nonlinear ICA
Hyvärinen, A. and Morioka, H · 2016
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Nonlinear ICA of temporally dependent stationary sources
Hyvärinen, A. and Morioka, H · 2017
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CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., and Girshick, R. B · 2017
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3d shapes dataset
Burgess, C. and Kim, H · 2018
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An efficient framework for learning sentence representations
Logeswaran, L. and Lee, H · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
Big self-supervised models are strong semi-supervised learners
Chen, T., Kornblith, S., Swersky, K., Norouzi, M., and Hinton, G. E · 2020
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Debiased contrastive learning
Chuang, C., Robinson, J., Lin, Y., Torralba, A., and Jegelka, S · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. B · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. B · 2020
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Data-efficient image recognition with contrastive predictive coding
Hénaff, O. J · 2020
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Variational autoencoders and nonlinear ICA: A unifying framework
Khemakhem, I., Kingma, D. P., Monti, R. P., and Hyvärinen, A · 2020
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Unsupervised feature learning via non-parametric instance discrimination
Wu, Z., Xiong, Y., Yu, S. X., and Lin, D · 2018
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Learning representations by maximizing mutual information across views
Bachman, P., Hjelm, R. D., and Buchwalter, W · 2019
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On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
Gondal, M. W., Wuthrich, M., Miladinovic, D., Locatello, F., Breidt, M., Volchkov, V., Akpo, J., Bachem, O., Schölkopf, B., and Bauer, S · 2019
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Learning deep representations by mutual information estimation and maximization
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., and Bengio, Y · 2019
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Nonlinear ICA using auxiliary variables and generalized contrastive learning
Hyvärinen, A., Sasaki, H., and Turner, R. E · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
Locatello, F., Bauer, S., Lucic, M., Rätsch, G., Gelly, S., Schölkopf, B., and Bachem, O · 2019
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Ice-beem: Identifiable conditional energy-based deep models based on nonlinear ICA
Khemakhem, I., Monti, R. P., Kingma, D. P., and Hyvärinen, A · 2020
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Weakly-supervised disentanglement without compromises
Locatello, F., Poole, B., Rätsch, G., Schölkopf, B., Bachem, O., and Tschannen, M · 2020
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Multi-task self-supervised learning for robust speech recognition
Ravanelli, M., Zhong, J., Pascual, S., Swietojanski, P., Monteiro, J., Trmal, J., and Bengio, Y · 2020
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Contrastive learning with hard negative samples
Robinson, J., Chuang, C.-Y., Sra, S., and Jegelka, S · 2020
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On linear identifiability of learned representations
Roeder, G., Metz, L., and Kingma, D. P · 2020
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What makes for good views for contrastive learning, 2020
Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., and Isola, P · 2020
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On mutual information maximization for representation learning
Tschannen, M., Djolonga, J., Rubenstein, P. K., Gelly, S., and Lucic, M · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Wang, T. and Isola, P · 2020
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On the importance of views in unsupervised representation learning
Wu, M., Zhuang, C., Yamins, D., and Goodman, N · 2020
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Blender - a 3D modelling and rendering package
Blender Online Community · 2021
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On the transfer of disentangled representations in realistic settings
Dittadi, A., Träuble, F., Locatello, F., Wüthrich, M., Agrawal, V., Winther, O., Bauer, S., and Schölkopf, B · 2021
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Towards nonlinear disentanglement in natural data with temporal sparse coding
Klindt, D., Schott, L., Sharma, Y., Ustyuzhaninov, I., Brendel, W., Bethge, M., and Paiton, D · 2021
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