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Self-supervised representation learning in computer vision relies heavily on hand-crafted image transformations to learn meaningful and invariant features.
The hungarian method for the assignment problem
Kuhn, H. W · 1955
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Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
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The mnist database of handwritten digits, 1998
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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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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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High-Content Phenotypic Profiling of Drug Response Signatures across Distinct Cancer Cells
Caie, P. D., Walls, R. E., Ingleston-Orme, A., Daya, S., Houslay, T., Eagle, R., Roberts, M. E., and Carragher, N. O · 2010
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Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance
Vinh, N. X., Epps, J., and Bailey, J · 2010
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Ressl: Relational self-supervised learning with weak augmentation
Zheng, M., You, S., Wang, F., Qian, C., Zhang, C., Wang, X., and Xu, C · 2010
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An analysis of single-layer networks in unsupervised feature learning
Coates, A., Ng, A., and Lee, H · 2011
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Annotated high-throughput microscopy image sets for validation
Ljosa, V., Sokolnicki, K. L., and Carpenter, A. E · 2012
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Deep clustering for unsupervised learning of visual features
Caron, M., Bojanowski, P., Joulin, A., and Douze, M · 2018
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Autoaugment: Learning augmentation policies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2019
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A theoretical analysis of contrastive unsupervised representation learning
Saunshi, N., Plevrakis, O., Arora, S., Khodak, M., and Khandeparkar, H · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A · 2020
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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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Bootstrap your own latent - a new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., Piot, B., kavukcuoglu, k., Munos, R., and Valko, M · 2020
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Dada: Differentiable automatic data augmentation
Li, Y., Hu, G., Wang, Y., Hospedales, T., Robertson, N. M., and Yang, Y · 2020
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Towards a hypothesis on visual transformation based self-supervision
Pal, D. K., Nallamothu, S., and Savvides, 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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Emerging properties in self-supervised vision transformers
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
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Contrastive learning with stronger augmentations, 2021
Wang, X. and Qi, G.-J · 2021
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Toward understanding the feature learning process of self-supervised contrastive learning
Wen, Z. and Li, Y · 2021
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What should not be contrastive in contrastive learning
Xiao, T., Wang, X., Efros, A. A., and Darrell, T · 2021
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Distribution estimation to automate transformation policies for self-supervision
Yang, S., Das, D., Chang, S., Yun, S., and Porikli, F · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S · 2021
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Exploring the limits of large scale pre-training
Abnar, S., Dehghani, M., Neyshabur, B., and Sedghi, H · 2022
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Chen, X. and He, K · 2021
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An empirical study of training self-supervised vision transformers
Chen, X., Xie, S., and He, K · 2021
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With a little help from my friends: Nearest-neighbor contrastive learning of visual representations
Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., and Zisserman, A · 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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Towards the generalization of contrastive self-supervised learning, 2021
Huang, W., Yi, M., and Zhao, X · 2021
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Computer vision self-supervised learning methods on time series, 2021
Lee, D. and Aune, E · 2021
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Direct differentiable augmentation search
Liu, A., Huang, Z., Huang, Z., and Wang, N · 2021
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The effects of regularization and data augmentation are class dependent
Balestriero, R., Bottou, L., and LeCun, Y · 2022
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Revealing invisible cell phenotypes with conditional generative modeling
Lamiable, A., Champetier, T., Leonardi, F., Cohen, E., Sommer, P., Hardy, D., Argy, N., Massougbodji, A., Del Nery, E., Cottrell, G., Kwon, Y.-J., and Genovesio, A · 2022
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Neural manifold clustering and embedding, 2022
Li, Z., Chen, Y., LeCun, Y., and Sommer, F. T · 2022
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Comparison of semi-supervised learning methods for high content screening quality control
Masud, U., Cohen, E., Bendidi, I., Bollot, G., and Genovesio, A · 2022
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Optimizing transformations for contrastive learning in a differentiable framework
Ruppli, C., Gori, P., Ardon, R., and Bloch, I · 2022
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Understanding contrastive learning requires incorporating inductive biases
Saunshi, N., Ash, J. T., Goel, S., Misra, D., Zhang, C., Arora, S., Kakade, S. M., and Krishnamurthy, A · 2022
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On the importance of hyperparameters and data augmentation for self-supervised learning
Wagner, D., Ferreira, F., Stoll, D., Schirrmeister, R. T., Müller, S., and Hutter, F · 2022
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Decoupled contrastive learning
Yeh, C.-H., Hong, C.-Y., Hsu, Y.-C., Liu, T.-L., Chen, Y., and LeCun, Y · 2022
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Rethinking the augmentation module in contrastive learning: Learning hierarchical augmentation invariance with expanded views
Zhang, J. and Ma, K · 2022
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Unpaired image-to-image translation with limited data to reveal subtle phenotypes
Bourou, A., Daupin, K., Dubreuil, V., Thonel, A. D., Lallemand-Mezger, V., and Genovesio, A · 2023
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