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Residual error based knowledge distillation

https://doi.org/10.1016/j.neucom.2020.10.113

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Self-supervised Knowledge Distillation Using Singular Value Decomposition

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Adversarial Network Compression

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ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

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Self-Referenced Deep Learning

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Deep learning for visual understanding: A review

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A survey of deep neural network architectures and their applications

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ImageNet: A large-scale hierarchical image database

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Deep Learning Face Representation from Predicting 10,000 Classes

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Accurate Image Super-Resolution Using Very Deep Convolutional Networks

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Deep Residual Learning for Image Recognition

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Densely Connected Convolutional Networks

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A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning

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Deep Mutual Learning

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Similarity-Preserving Knowledge Distillation

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Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching

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Fixed-point feedforward deep neural network design using weights +1, 0, and −1

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Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

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Towards Evaluating the Robustness of Neural Networks

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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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Model compression

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ImageNet classification with deep convolutional neural networks

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Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons

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Improved Knowledge Distillation via Teacher Assistant

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Progressive Blockwise Knowledge Distillation for Neural Network Acceleration

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Do Deep Nets Really Need to be Deep?

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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FitNets: Hints for Thin Deep Nets

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Distilling the Knowledge in a Neural Network

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Multimodal Residual Learning for Visual QA

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Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer

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N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning

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Paraphrasing Complex Network: Network Compression via Factor Transfer

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Born Again Neural Networks

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Knowledge Distillation by On-the-Fly Native Ensemble

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