Summary of Automatic Channel Pruning For Multi-head Attention, by Eunho Lee and Youngbae Hwang
Automatic Channel Pruning for Multi-Head Attention
by Eunho Lee, Youngbae Hwang
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper addresses the challenge of applying Transformers to vision tasks, where their quadratic computation complexity hinders performance. The authors propose an automatic channel pruning method for multi-head attention, which involves incorporating channel similarity-based weights into the pruning indicator to preserve informative channels. They also adjust the pruning indicator to enforce equal channel removal across all heads, preventing misalignment. Additionally, they introduce a reweight module to compensate for information loss and an initialization step based on attention differences between original structures and each channel. The proposed method outperforms previous state-of-the-art efficient models and pruned methods on ImageNet-1K, with FLattenTransformer showing improved accuracy across various MACs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper tries to make a special kind of AI called Transformers work better for vision tasks like recognizing images. One problem is that these AI models are too slow because they need to process lots of information at once. The scientists developed a new way to remove some of this extra information, so the AI can be faster without losing its ability to recognize things accurately. They tested their method on a big image recognition dataset and found that it worked better than other similar methods. |
Keywords
» Artificial intelligence » Attention » Multi head attention » Pruning
