In this episode we discuss Practical Network Acceleration with Tiny Sets
by Guo-Hua Wang, Jianxin Wu. The paper proposes a new method called PRACTISE for accelerating networks using only small training sets. It suggests dropping blocks as a better approach than filter-level pruning for achieving higher acceleration ratio and improved latency-accuracy performance under few-shot settings. The paper introduces a new concept called "recoverability" to measure the difficulty of recovering the compressed network and proposes an algorithm using it to select which blocks to drop. PRACTISE outperforms previous methods by a significant margin and also shows high generalization ability under data-free or out-of-domain data settings.
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