Summary of Flatten Long-range Loss Landscapes For Cross-domain Few-shot Learning, by Yixiong Zou et al.
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning
by Yixiong Zou, Yicong Liu, Yiman Hu, Yuhua Li, Ruixuan Li
First submitted to arxiv on: 1 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 The proposed cross-domain few-shot learning (CDFSL) framework aims to transfer knowledge from source domains with abundant training samples to target domains with limited data. To address challenges in transferring and fine-tuning models, researchers extend the analysis of loss landscapes from the parameter space to the representation space. They observe that sharp minima result in hard-to-transfer representations and introduce a simple approach to achieve long-range flattening by randomly sampling interpolated representations. This method replaces the original normalization layer in CNNs and ViTs, adding only minimal parameters. The framework outperforms state-of-the-art methods on 8 datasets, with performance improvements of up to 9% compared to current best approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cross-domain few-shot learning is a way for machines to learn from limited data by using knowledge from other areas. This helps when there’s not much information available about what you’re trying to learn. The problem is that it’s hard to transfer this knowledge and make it work well in the new area. Researchers found out why this happens and came up with a simple solution to fix it. They made a new layer in the machine learning model that helps the knowledge transfer better. This worked really well, making it possible for machines to learn from limited data more accurately. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Machine learning
