[CVPR 2020] Model Compression Paper List

Neural Network Pruning With Residual-Connection and Limited-Data

Multi-Dimensional Pruning: A Unifed Frameworkd for Model Compression

HRank: Filter Pruning Using High-Rank Feature Map

DMCP: Differentaible Markov Channel Pruning for Neural Networks

Structured Compression by Weight Encryption for Unstructured Pruning and Quantization

Learning Filter Pruning Criteria for Deep Convolutional Neural Ntworks Acceleration

APQ: Joint Search for Network Architecture, Pruning, and Quantization Policy

Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression

Do We Really Need Multiplications in Deep Learning?

ReSprop: Reuse Sparsified Backpropagation

NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks