NETWORK COMPRESSION USING CORRELATION ANALYSIS OF LAYER RESPONSES

By Xavier Suau, Luca Zappella, Nicholas Apostoloff. 2018

22 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jonathan Frankle
2. Zhuang Liu
3. Trevor Darrell
4. Jiahui Yu
5. Quoc V. Le
6. Yoshua Bengio
7. Mingjie Sun
8. Zhe Gan

Citations in the corpus

• What is the State of Neural Network Pruning? (2020, methodology): “…many methods produce pruned models that outperform re-training from scratch with the same sparsity pattern (Zhang et al., 2015; Yu et al., 2018; Louizos et al., 2017; He et al., 2017; Luo et al., 2017; Frankle & Carbin, 2019) (at least with a large enough amount of pruning (Suau et al., 2018)).”
• Rethinking the Value of Network Pruning (2018, background): “…after pruning are usually considered to be critical, as how to accurately select the set of important weights is a very active research topic in the literature (Han et al., 2015; Molchanov et al., 2016; Li et al., 2017; Luo et al., 2017; He et al., 2017b; Liu et al., 2017; Suau et al., 2018).”