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Learning visual representations with interpretable features, i.e., disentangled representations, remains a challenging problem.
Nonlinear independent component analysis: Existence and uniqueness results
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Learning multiple layers of features from tiny images
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Nonlinear mixtures
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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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Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., and Efros, A. A · 2015
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Imagenet large scale visual recognition challenge
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Large-scale classification of fine-art paintings: Learning the right metric on the right feature
Saleh, B. and Elgammal, A · 2015
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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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Arbitrary style transfer in real-time with adaptive instance normalization
Huang, X. and Belongie, S · 2017
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Recognition in terra incognita
Beery, S., Van Horn, G., and Perona, P · 2018
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Multi-level variational autoencoder: Learning disentangled representations from grouped observations
Bouchacourt, D., Tomioka, R., and Nowozin, S · 2018
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How to read paintings: semantic art understanding with multi-modal retrieval
Garcia, N. and Vogiatzis, G · 2018
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Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2018
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Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Hendrycks, D. and Dietterich, T · 2018
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Towards a definition of disentangled representations
Higgins, I., Amos, D., Pfau, D., Racaniere, S., Matthey, L., Rezende, D., and Lerchner, A · 2018
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Group-based learning of disentangled representations with generalizability for novel contents
Hosoya, H · 2018
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Disentangled representation learning for non-parallel text style transfer
John, V., Mou, L., Bahuleyan, H., and Vechtomova, O · 2018
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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., and Sayres, R · 2018
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Diverse image-to-image translation via disentangled representations
Lee, H.-Y., Tseng, H.-Y., Huang, J.-B., Singh, M., and Yang, M.-H · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
Weakly-supervised disentanglement without compromises
Locatello, F., Poole, B., Rätsch, G., Schölkopf, B., Bachem, O., and Tschannen, M · 2020
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Adversarial disentanglement with grouped observations
Nemeth, J · 2020
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A survey on data augmentation for text classification
Bayer, M., Kaufhold, M.-A., and Reuter, C · 2021
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Exploring simple siamese representation learning
Chen, X. and He, K · 2021
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Self-supervised learning with data augmentations provably isolates content from style
Kügelgen, J., Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve, M., and Locatello, F · 2021
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Post-processing for individual fairness
Petersen, F., Mukherjee, D., Sun, Y., and Yurochkin, M · 2021
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Learning disentangled representations for recommendation
Ma, J., Zhou, C., Cui, P., Yang, H., and Zhu, W · 2019
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Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2019
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Weakly supervised disentanglement with guarantees
Shu, R., Chen, Y., Kumar, A., Ermon, S., and Poole, B · 2019
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Energy and Policy Considerations for Deep Learning in NLP
Strubell, E., Ganesh, A., and McCallum, A · 2019
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Optimized Score Transformation for Fair Classification
Wei, D., Ramamurthy, K. N., and Calmon, F. d. P · 2019
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Eda: Easy data augmentation techniques for boosting performance on text classification tasks
Wei, J. and Zou, K · 2019
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Disentangling content and style via unsupervised geometry distillation
Wu, W., Cao, K., Li, C., Qian, C., and Loy, C. C · 2019
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Rethinking content and style: exploring bias for unsupervised disentanglement
Ren, X., Yang, T., Wang, Y., and Zeng, W · 2021
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Aladin: all layer adaptive instance normalization for fine-grained style similarity
Ruta, D., Motiian, S., Faieta, B., Lin, Z., Jin, H., Filipkowski, A., Gilbert, A., and Collomosse, J · 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
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Text data augmentation for deep learning
Shorten, C., Khoshgoftaar, T. M., and Furht, B · 2021
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Self-supervised learning disentangled group representation as feature
Wang, T., Yue, Z., Huang, J., Sun, Q., and Zhang, H · 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
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Leveraging content-style item representation for visual recommendation
Deldjoo, Y., Di Noia, T., Malitesta, D., and Merra, F. A · 2022
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Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
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Stylebabel: Artistic style tagging and captioning
Ruta, D., Gilbert, A., Aggarwal, P., Marri, N., Kale, A., Briggs, J., Speed, C., Jin, H., Faieta, B., Filipkowski, A., et al · 2022
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Genome-wide association analysis by lasso penalized logistic regression
Wu, T. T., Chen, Y. F., Hastie, T., Sobel, E., and Lange, K · 2059
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