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The generalization capability of deep neural networks has been substantially improved by applying a wide spectrum of regularization methods, e.g., restricting function space, injecting randomness during training, augmenting data, etc.
The comparison and evaluation of forecasters
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
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Multi30k: Multilingual english-german image descriptions
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Deep residual learning for image recognition
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Identity mappings in deep residual networks
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Disturblabel: Regularizing CNN on the loss layer
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Bias-reduced uncertainty estimation for deep neural classifiers
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Self-knowledge distillation in natural language processing
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
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fairseq: A fast, extensible toolkit for sequence modeling
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PyTorch: An imperative style, high-performance deep learning library
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Softtarget regularization: An effective technique to reduce over-fitting in neural networks
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On calibration of modern neural networks
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Deep pyramidal residual networks
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Mask r-cnn
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Densely connected convolutional networks
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Attention is all you need
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Regularized evolution for image classifier architecture search
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EfficientNet: Rethinking model scaling for convolutional neural networks
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Pay less attention with lightweight and dynamic convolutions
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Data-distortion guided self-distillation for deep neural networks
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Shakedrop regularization for deep residual learning
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Snapshot distillation: Teacher-student optimization in one generation
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Cutmix: Regularization strategy to train strong classifiers with localizable features
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Be your own teacher: Improve the performance of convolutional neural networks via self distillation
L. Zhang, J. Song, A. Gao, J. Chen, C. Bao, and K. Ma · 2019
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Augmix: A simple method to improve robustness and uncertainty under data shift
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Revisiting knowledge distillation via label smoothing regularization
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Regularizing class-wise predictions via self-knowledge distillation
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Understanding and improving knowledge distillation
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