Summary of Advanced Knowledge Transfer: Refined Feature Distillation For Zero-shot Quantization in Edge Computing, by Inpyo Hong et al.
Advanced Knowledge Transfer: Refined Feature Distillation for Zero-Shot Quantization in Edge Computing
by Inpyo Hong, Youngwan Jo, Hyojeong Lee, Sunghyun Ahn, Sanghyun Park
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 The proposed AKT method enhances the training ability of low-bit quantized models in zero-shot quantization by effectively transferring knowledge from full-precision models. This is achieved through refining feature maps in the feature distillation process, which addresses the fundamental gradient exploding problem in low-bit models. The method utilizes both spatial and channel attention information, demonstrating significant performance enhancements in existing generative models. Experimental results on CIFAR-10 and CIFAR-100 datasets show state-of-the-art accuracy improvements in 3,5bit scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AKT is a new way to help small computers learn from bigger ones. When we try to make models work on tiny devices, they often don’t perform as well as the original model. AKT fixes this by taking the important parts of the big model and copying them over to the small model. This helps the small model do better in situations where it didn’t know what to do before. |
Keywords
» Artificial intelligence » Attention » Distillation » Precision » Quantization » Zero shot
