DiffRate : Differentiable Compression Rate for Efficient Vision
  Transformers
Mengzhao Chen Wenqi Shao Peng Xu Mingbao Lin Kaipeng Zhang Fei Chao Rongrong Ji Yu Qiao Ping Luo
Token compression aims to speed up large-scale vision transformers (e.g.
ViTs) by pruning (dropping) or merging tokens. It is an important but
challenging task. Although recent advanced approaches achieved great success,
they need to carefully handcraft a compression rate (i.e. number of tokens to
remove), which is tedious and leads to sub-optimal performance. To tackle this
problem, we propose Differentiable Compression Rate (DiffRate), a novel token
compression method that has several appealing properties prior arts do not
have. First, DiffRate enables propagating the loss function's gradient onto the
compression ratio, which is considered as a non-differentiable hyperparameter
in previous work. In this case, different layers can automatically learn
different compression rates layer-wisely without extra overhead. Second, token
pruning and merging can be naturally performed simultaneously in DiffRate,
while they were isolated in previous works. Third, extensive experiments
demonstrate that DiffRate achieves state-of-the-art performance. For example,
by applying the learned layer-wise compression rates to an off-the-shelf ViT-H
(MAE) model, we achieve a 40% FLOPs reduction and a 1.5x throughput
improvement, with a minor accuracy drop of 0.16% on ImageNet without
fine-tuning, even outperforming previous methods with fine-tuning. Codes and
models are available at https://github.com/OpenGVLab/DiffRate.