Summary of On Improving the Algorithm-, Model-, and Data- Efficiency Of Self-supervised Learning, by Yun-hao Cao and Jianxin Wu
On Improving the Algorithm-, Model-, and Data- Efficiency of Self-Supervised Learning
by Yun-Hao Cao, Jianxin Wu
First submitted to arxiv on: 30 Apr 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 A novel self-supervised learning (SSL) approach is proposed to improve the efficiency of SSL methods in large-scale datasets and practical applications. The method, based on non-parametric instance discrimination, uses a single branch and corrects the memory bank update rule for improved performance. A new self-distillation loss is also introduced, which minimizes the KL divergence between probability distributions and accelerates convergence. Experimental results show that this approach outperforms various baselines with significantly less overhead, particularly in scenarios with limited data and small models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Self-supervised learning has made big progress recently! The problem is that most methods are too computationally expensive and need lots of images to work well. In this paper, the authors create a new method called non-parametric instance discrimination, which uses less computer power and can work with smaller amounts of data. They also introduce a way to make the model learn faster by using something called self-distillation loss. The results show that their approach works better than others with much less computational effort. |
Keywords
» Artificial intelligence » Distillation » Probability » Self supervised
