Summary of A Generalization Theory Of Cross-modality Distillation with Contrastive Learning, by Hangyu Lin et al.
A Generalization Theory of Cross-Modality Distillation with Contrastive Learning
by Hangyu Lin, Chen Liu, Chengming Xu, Zhengqi Gao, Yanwei Fu, Yuan Yao
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 In this paper, researchers tackle the problem of distilling knowledge from limited data sources like depth maps and high-quality sketches. They propose a general framework for cross-modality contrastive distillation (CMCD) that combines positive and negative correspondence to improve feature learning. The authors also provide theoretical insights into how the distance between source and target modalities affects test error on downstream tasks, which is validated by experimental results. Their CMCD algorithm outperforms existing methods by a margin of 2-3% across various modalities (image, sketch, depth map, and audio) and tasks (recognition and segmentation). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn from limited data sources without labels. Imagine you have a picture and a drawing that looks similar. The researchers developed a way to use these similarities to teach machines about the world, even when we don’t have labeled training data. This is important because it can help with privacy and memory issues. They created a new approach called cross-modality contrastive distillation (CMCD) that does better than other methods at learning generalizable features. This means that CMCD can be used for various tasks like recognizing objects or segmenting images. |
Keywords
» Artificial intelligence » Distillation
