Summary of What Makes Clip More Robust to Long-tailed Pre-training Data? a Controlled Study For Transferable Insights, by Xin Wen et al.
What Makes CLIP More Robust to Long-Tailed Pre-Training Data? A Controlled Study for Transferable Insights
by Xin Wen, Bingchen Zhao, Yilun Chen, Jiangmiao Pang, Xiaojuan Qi
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 paper investigates the robustness of CLIP, a pre-trained vision-language model, to severe data imbalance in web-scale datasets. Contrary to expectations, CLIP exhibits notable robustness and effectiveness in learning generalizable representations compared to supervised learning. To understand this finding, the authors conduct controlled experiments and reveal that CLIP’s pretext task forms a dynamic classification problem, isolating bias from dominant classes and implicitly balancing the learning signal. The study shows that CLIP’s robustness and discriminability improve with more descriptive language supervision, larger data scale, and broader open-world concepts. These findings provide transferable insights for the research community, enabling models trained on imbalanced data to achieve CLIP-level performance on diverse recognition tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to teach a computer to recognize things like cats and dogs from a huge dataset that’s really unbalanced – some categories have way more examples than others. Surprisingly, a pre-trained model called CLIP does quite well even with this imbalance. The researchers wanted to know why this was the case so they ran experiments to figure it out. They found that the way CLIP is trained helps balance out the learning process, making it more robust and accurate. This discovery can help other AI models learn better from imbalanced data too. |
Keywords
» Artificial intelligence » Classification » Language model » Supervised
