Summary of Simple Unsupervised Knowledge Distillation with Space Similarity, by Aditya Singh and Haohan Wang
Simple Unsupervised Knowledge Distillation With Space Similarity
by Aditya Singh, Haohan Wang
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper proposes an unsupervised knowledge distillation method that directly motivates a smaller neural network (student) to model the embedding manifold of a larger, labeled teacher network. The approach is designed to preserve all inter/intra sample relationships in the teacher’s latent space by introducing a new loss component called “space similarity.” This loss encourages each dimension of the student’s feature space to be similar to its corresponding dimension in the teacher’s features. The authors demonstrate the effectiveness of their method on various benchmarks, showcasing strong performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to teach smaller artificial intelligence models to learn from larger ones without needing labels. Currently, we don’t do a great job at this with smaller models. To fix this, researchers are trying different approaches that focus on relationships between samples. Instead of doing that, this paper takes a different approach by teaching the smaller model to mimic the way the larger model understands its data. This helps keep all the important relationships intact. The authors show that their method works well on many tasks. |
Keywords
» Artificial intelligence » Embedding » Knowledge distillation » Latent space » Neural network » Unsupervised
