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Self-supervised learning, which learns by constructing artificial labels given only the input signals, has recently gained considerable attention for learning representations with unlabeled datasets, i.e., learning without any human-annotated supervision.
Gradient-based learning applied to document recognition
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Visualizing data using t-sne
Maaten, L. v. d. and Hinton, G · 2008
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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., Hinton, G., et al · 2009
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Recognizing indoor scenes
Quattoni, A. and Torralba, A · 2009
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Novel dataset for fine-grained image categorization
Khosla, A., Jayadevaprakash, N., Yao, B., and Fei-Fei, L · 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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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Be your own teacher: Improve the performance of convolutional neural networks via self distillation
Zhang, L., Song, J., Gao, A., Chen, J., Bao, C., and Ma, K · 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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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 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 learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P · 2016
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Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al · 2016
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Unsupervised learning by predicting noise
Bojanowski, P. and Joulin, A · 2017
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Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Deep pyramidal residual networks
Han, D., Kim, J., and Kim, J · 2017
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Colorization as a proxy task for visual understanding
Larsson, G., Maire, M., and Shakhnarovich, G · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
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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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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
Learning imbalanced datasets with label-distribution-aware margin loss
Cao, K., Wei, C., Gaidon, A., Arechiga, N., and Ma, T · 2019
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Self-supervised gans via auxiliary rotation loss
Chen, T., Zhai, X., Ritter, M., Lucic, M., and Houlsby, N · 2019
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Autoaugment: Learning augmentation strategies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2019
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Class-balanced loss based on effective number of samples
Cui, Y., Jia, M., Lin, T.-Y., Song, Y., and Belongie, S · 2019
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Using self-supervised learning can improve model robustness and uncertainty
Hendrycks, D., Mazeika, M., Kadavath, S., and Song, D · 2019
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Meta-learning with differentiable convex optimization
Lee, K., Maji, S., Ravichandran, A., and Soatto, S · 2019
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Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N · 2018
Cited alongside, same era.
Knowledge distillation by on-the-fly native ensemble
Lan, X., Zhu, X., and Gong, S · 2018
Cited alongside, same era.
A simple neural attentive meta-learner
Mishra, N., Rohaninejad, M., Chen, X., and Abbeel, P · 2018
Cited alongside, same era.
Tadam: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B., López, P. R., and Lacoste, A · 2018
Cited alongside, same era.
Learning to compare: Relation network for few-shot learning
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H., and Hospedales, T. M · 2018
Cited alongside, same era.
The inaturalist species classification and detection dataset
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., and Belongie, S · 2018
Cited alongside, same era.
Fast autoaugment
Lim, S., Kim, I., Kim, T., Kim, C., and Kim, S · 2019
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Relational knowledge distillation
Park, W., Kim, D., Lu, Y., and Cho, M · 2019
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Meta-learning with latent embedding optimization
Rusu, A. A., Rao, D., Sygnowski, J., Vinyals, O., Pascanu, R., Osindero, S., and Hadsell, R · 2019
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Data-distortion guided self-distillation for deep neural networks
Xu, T.-B. and Liu, C.-L · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y · 2019
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S 4 L: Self-supervised semi-supervised learning
Zhai, X., Oliver, A., Kolesnikov, A., and Beyer, L · 2019
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Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring
Berthelot, D., Carlini, N., Cubuk, E. D., Kurakin, A., Sohn, K., Zhang, H., and Raffel, C · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Contrastive representation distillation
Tian, Y., Krishnan, D., and Isola, P · 2020
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Self-labelling via simultaneous clustering and representation learning
YM., A., C., R., and A., V · 2020
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