Summary of Continual All-in-one Adverse Weather Removal with Knowledge Replay on a Unified Network Structure, by De Cheng et al.
Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network Structure
by De Cheng, Yanling Ji, Dong Gong, Yan Li, Nannan Wang, Junwei Han, Dingwen Zhang
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel continual learning framework with effective knowledge replay (KR) is proposed for the all-in-one adverse weather removal task. The framework, which combines a principal component projection and an effective knowledge distillation mechanism, is designed to learn from incremental data collections reflecting various degeneration types in real-world applications. The approach enables sharing and accumulation of knowledge across different degenerations within a unified network structure. Experimental results demonstrate the effectiveness of the proposed method, competing with existing dedicated or joint training image restoration methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to remove bad weather from images is being developed. Right now, it’s hard to make systems that work well in real-world environments because they often encounter unexpected weather conditions. Existing methods are designed to learn from all the data at once, but this isn’t realistic because we can’t always collect all the data before starting the learning process. To fix this issue, a new framework is being created that allows for incremental learning and sharing of knowledge across different types of weather degradations. |
Keywords
» Artificial intelligence » Continual learning » Knowledge distillation
