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Data augmentation is critical to the success of modern deep learning techniques.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
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The cifar-10 dataset
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Convolutional neural fabrics
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Mastering the game of go with deep neural networks and tree search
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Barret Zoph and Quoc V Le · 2016
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mixup: Beyond empirical risk minimization
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Data augmentation in emotion classification using generative adversarial networks
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Learning transferable architectures for scalable image recognition
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Autoaugment: Learning augmentation policies from data
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Irlas: Inverse reinforcement learning for architecture search
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Squeeze-and-excitation networks
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Progressive neural architecture search
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Neural architecture optimization
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Efficient neural architecture search via parameter sharing
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Regularized evolution for image classifier architecture search
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