Summary of Transfer Learning with Informative Priors: Simple Baselines Better Than Previously Reported, by Ethan Harvey et al.
Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported
by Ethan Harvey, Mikhail Petrov, Michael C. Hughes
First submitted to arxiv on: 24 May 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 This study explores transfer learning strategies to improve classifier accuracy on a target task with limited labeled data. The authors investigate the effectiveness of using a source task to learn a prior distribution over neural network weights, rather than just an initialization. They compare transfer learning methods with and without informative priors across five datasets, finding that standard transfer learning performs better than previously reported. However, the use of informative priors yields varying gains in accuracy, ranging from modest to substantial, depending on the dataset. Notably, an isotropic covariance prior emerges as a competitive yet simpler alternative to learned low-rank covariance matrices. The study also analyzes the empirical loss landscapes and finds high variability, contradicting the hypothesized improved alignment between train and test loss landscapes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn from one task to do better on another task with few examples. It looks at how using information from a source task can improve results. Researchers compared different methods and found that using some prior knowledge is helpful, but it depends on the specific problem. In some cases, just starting with a good initialization works best. They also found that making assumptions about what the data looks like can be useful. This research could lead to better AI models in areas like image recognition or natural language processing. |
Keywords
» Artificial intelligence » Alignment » Natural language processing » Neural network » Transfer learning
