of the proposed method are listed as below:
There are two key components of the method, namely, the attention computation and learn-to-rank module. For the first component, it is a common practice to compute importance using SE blocks. Therefore, the novelty of this component is limited.
Some important SOTAs are missing and some of them as below outperform the proposed method: (1) Ding, Xiaohan, et al. "Resrep: Lossless cnn pruning via decoupling remembering and forgetting." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021. (2) Li, Bailin, et al. "Eagleeye: Fast sub-net evaluation for efficient neural network pruning." European conference on computer vision. Springer, Cham, 2020. (3) Ruan, Xiaofeng, et al. "DPFPS: dynamic and progressive filter pruning for compressing convolutional neural networks from scratch." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 3. 2021.
Competing dynamic-pruning methods are kind of out-of-date. More recent works should be included.
Only results on small scale datasets are provided. Results on large scale datasets including ImageNet should be included to further verify the effectiveness of the proposed method.
35. No.3. 2021. Competing dynamic-pruning methods are kind of out-of-date. More recent works should be included. Only results on small scale datasets are provided. Results on large scale datasets including ImageNet should be included to further verify the effectiveness of the proposed method.