Dense Vision Transformer Compression with Few Samples
Hanxiao Zhang Yifan Zhou Guo-Hua Wang Jianxin Wu
Few-shot model compression aims to compress a large model into a more compact
one with only a tiny training set (even without labels). Block-level pruning
has recently emerged as a leading technique in achieving high accuracy and low
latency in few-shot CNN compression. But, few-shot compression for Vision
Transformers (ViT) remains largely unexplored, which presents a new challenge.
In particular, the issue of sparse compression exists in traditional CNN
few-shot methods, which can only produce very few compressed models of
different model sizes. This paper proposes a novel framework for few-shot ViT
compression named DC-ViT. Instead of dropping the entire block, DC-ViT
selectively eliminates the attention module while retaining and reusing
portions of the MLP module. DC-ViT enables dense compression, which outputs
numerous compressed models that densely populate the range of model complexity.
DC-ViT outperforms state-of-the-art few-shot compression methods by a
significant margin of 10 percentage points, along with lower latency in the
compression of ViT and its variants.