Summary of Optimizing V-information For Self-supervised Pre-training Data-effective Medical Foundation Models, by Wenxuan Yang et al.
Optimizing V-information for Self-Supervised Pre-training Data-Effective Medical Foundation Models
by Wenxuan Yang, Hanyu Zhang, Weimin Tan, Yuqi Sun, Bo Yan
First submitted to arxiv on: 13 Aug 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 abstract discusses recent research questioning the notion that increasing pre-training data always leads to enhanced model performance. To address this issue, data-effective learning approaches have been introduced to select valuable samples for foundation model pre-training. The authors propose a novel method called OptiDEL (Optimal Data-Effective Learning) that optimizes V-information in self-supervised pre-training of foundation models. This approach generates more diverse and challenging samples, achieving or even exceeding the performance of models trained on full datasets while using substantially less data. The OptiDEL method outperforms existing approaches across eight different datasets, with foundation models trained on only 5% of the pre-training data surpassing those trained on the full dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make medical AI models better by finding the right data to train them. Usually, people think that using more data makes the model better, but some research says this isn’t always true. To fix this, new methods have been developed to pick the most important data points for training. The authors came up with a new way called OptiDEL that helps find these valuable samples. It does this by looking at something called V-information and using it to choose the best data points. This means that even if there isn’t as much data, the model can still perform well. In fact, some models trained on only 5% of the data did better than those trained on all the data. |
Keywords
* Artificial intelligence * Self supervised
