Summary of In Search Of the Successful Interpolation: on the Role Of Sharpness in Clip Generalization, by Alireza Abdollahpoorrostam
In Search of the Successful Interpolation: On the Role of Sharpness in CLIP Generalization
by Alireza Abdollahpoorrostam
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Robust Fine-Tuning (RFT) method, which interpolates between zero-shot and fine-tuned models, aims to improve out-of-distribution (OOD) robustness in CLIP models. While RFT has been shown to be effective, understanding when it actually improves OOD error remains limited. This work investigates the robustness of RFT in CLIP models, focusing on the sharpness of the model during interpolation. The results demonstrate that layer-wise sharpness can reliably capture generalization performance on OOD data and correlates with accuracy for RFT. Furthermore, inducing sparsity in straggler layers can mitigate the failure mode phenomenon. This study provides new insights into the role of sharpness in the success of interpolation in CLIP foundation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RFT is a method that helps improve how well AI models work on new data they haven’t seen before. The authors wanted to see if RFT really makes AI models better at handling new situations, and what happens when they use it with a type of AI model called CLIP. They found that the way the model’s “sharpness” changes during this process can help predict how well it will work on new data. They also showed that making some parts of the model more important than others can make RFT even better. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Zero shot
