Summary of Bridging Diversity and Uncertainty in Active Learning with Self-supervised Pre-training, by Paul Doucet et al.
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Training
by Paul Doucet, Benjamin Estermann, Till Aczel, Roger Wattenhofer
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 study proposes a novel integration of diversity-based and uncertainty-based sampling strategies for active learning within self-supervised pre-trained models. The authors introduce the TypiClust-Margin (TCM) heuristic, which addresses the cold start problem while maintaining strong performance across various datasets in both low and high data regimes. By combining TypiClust for diversity sampling and Margin for uncertainty sampling, TCM effectively leverages the strengths of both approaches. Experimental results demonstrate that TCM outperforms existing methods across multiple datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps computers learn better by combining two ways to choose which examples to use during training. The new method, called TCM, starts by grouping similar examples together and then chooses the most uncertain ones. This helps the computer learn faster and make fewer mistakes. The results show that TCM works well on many datasets. |
Keywords
* Artificial intelligence * Active learning * Self supervised
