Summary of Semi-supervised Fine-tuning Of Vision Foundation Models with Content-style Decomposition, by Mariia Drozdova et al.
Semi-Supervised Fine-Tuning of Vision Foundation Models with Content-Style Decomposition
by Mariia Drozdova, Vitaliy Kinakh, Yury Belousov, Erica Lastufka, Slava Voloshynovskiy
First submitted to arxiv on: 2 Oct 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 semi-supervised fine-tuning approach enhances the performance of pre-trained foundation models on downstream tasks with limited labeled data. By leveraging content-style decomposition within an information-theoretic framework, the method improves latent representations, aligning them with specific task objectives and addressing distribution shift. The approach is evaluated on multiple datasets, including MNIST, CIFAR-10, SVHN, and GalaxyMNIST, showing improvements over supervised fine-tuning baselines in low-labeled data regimes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to make pre-trained models work better on specific tasks when we don’t have much labeled data. It’s like a special tool that helps the model learn more effectively from limited information. The authors test this approach on several datasets and show that it can improve performance, especially when there’s not much labeled data available. |
Keywords
» Artificial intelligence » Fine tuning » Semi supervised » Supervised
