Summary of Svasp: Self-versatility Adversarial Style Perturbation For Cross-domain Few-shot Learning, by Wenqian Li et al.
SVasP: Self-Versatility Adversarial Style Perturbation for Cross-Domain Few-Shot Learning
by Wenqian Li, Pengfei Fang, Hui Xue
First submitted to arxiv on: 12 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 Cross-Domain Few-Shot Learning (CD-FSL) method aims to transfer knowledge from seen source domains to unseen target domains, enhancing the generalization and robustness of models. A novel crop-global style perturbation approach called SVasP is introduced, which resolves issues of gradient instability and local optimization by simulating diverse potential target domain adversarial styles via input pattern diversification and localized crop style gradients. This method maximizes visual discrepancy while maintaining semantic consistency between global, crop, and adversarial features, resulting in a flattened minima in the loss landscape that boosts model transferability. Extensive experiments on multiple benchmark datasets demonstrate SVasP’s superiority over existing state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps models learn from one place and use what they’ve learned somewhere else. This is important because it makes sure models are good at generalizing, not just memorizing information. The new method called SVasP fixes some big problems with previous approaches by making the learning process more stable and reliable. It does this by changing how the model looks at different parts of an image and combining those changes to create a more diverse set of possibilities. |
Keywords
» Artificial intelligence » Few shot » Generalization » Optimization » Transferability
