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In self-supervised learning, one trains a model to solve a so-called pretext task on a dataset without the need for human annotation.
Histograms of oriented gradients for human detection
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Learning to learn: Model regression networks for easy small sample learning
Y. Wang and M. Hebert · 2016
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Y.-X. Wang and M. Hebert · 2016
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Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
R. Zhang, P. Isola, and A. A. Efros · 2016
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Lstm self-supervision for detailed behavior analysis
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Data-dependent initializations of convolutional neural networks
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G. Larsson, M. Maire, and G. Shakhnarovich · 2016
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J. Donahue, P. Krähenbühl, and T. Darrell · 2017
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Represenation learning by learning to count
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Transitive invariance for self-supervised visual representation learning
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Few-shot hash learning for image retrieval
Y.-X. Wang, L. Gui, and M. Hebert · 2017
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Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
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Understanding deep learning requires rethinking generalization
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