Paper list for deep neural network compression and acceleration
- Low-Rank Expansions
- Jaderberg et al., "Speeding up Convolutional Neural Networks with Low Rank Expansions". BMVC2014
- SVD
- Denton et al, "Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation". NIPS2014
- Zhang et al., “Accelerating Very Deep Convolutional Networks for Classification and Detection”. TPAMI 2016.
- CP Decomposition
- Lebedev et al., “Speeding-up Convolutional Neural Networks using Fine-tuned CP-Decomposition”. ICLR 2015
- Tucker Decomposition
- Kim et al., “Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications”. ICLR 2016
- Block Term Decomposition
- Wang et al., “Accelerating Convolutional Neural Networks for Mobile Applications”. MM 2016.
- Ye et al., “Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition ”. CVPR2018.
- Tensor Train Decomposition
- Novikov et al., “Tensorizing Neural Networks”. NIPS 2016.
- Tensor Ring (TR) Factorization
- Wang et al., “Wide Compression: Tensor Ring Nets”. CVPR2018