Summary of Early Period Of Training Impacts Adaptation For Out-of-distribution Generalization: An Empirical Study, by Chen Cecilia Liu and Iryna Gurevych
Early Period of Training Impacts Adaptation for Out-of-Distribution Generalization: An Empirical Study
by Chen Cecilia Liu, Iryna Gurevych
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 investigates the relationship between early neural network training and out-of-distribution (OOD) generalization. Prior research has shown that early learning dynamics impact in-distribution performance, but the implications for OOD generalization remain unclear due to analytical limitations. The authors utilize trace of Fisher Information and sharpness as indicators to study gradual unfreezing, a methodology that progressively unfrees parameters during training. Empirical experiments demonstrate that 1) changing the number of trainable parameters via gradual unfreezing can improve OOD results; 2) trace of Fisher Information and sharpness can indicate optimal removal of gradual unfreezing for better OOD generalization. Experiments on image and text data show that early learning dynamics provide Pareto improvements in ID and OOD performance with minimal complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial neural networks learn and perform when they’re shown new, unexpected data. Researchers have found that the way a network learns at the beginning affects its performance on familiar data, but it’s not clear how this impacts its ability to handle unfamiliar data. The authors use special tools to study how changing the network’s training method during the early stages can improve its performance on both familiar and new data. Their experiments show that making small changes to the network during training can help it do better on new data, while also improving its performance on familiar tasks. This is an important area of research because it could help us create more powerful and flexible AI systems. |
Keywords
* Artificial intelligence * Generalization * Neural network
