Early Period of Training Impacts Adaptation for Out-of-Distribution Generalization: An Empirical Study
This work addresses the challenge of improving OOD generalization for neural networks, which is crucial for real-world applications where data distributions shift, but it is incremental as it builds on prior insights about early training impacts.
The study tackled the problem of how early training dynamics affect out-of-distribution (OOD) generalization under covariate shift, finding that adjusting trainable parameters via gradual unfreezing during this period can significantly improve OOD results and that metrics like Fisher Information trace and sharpness serve as effective indicators for optimization.
Prior research shows that differences in the early period of neural network training significantly impact the performance of in-distribution (ID) data of tasks. Yet, the implications of early learning dynamics on out-of-distribution (OOD) generalization remain poorly understood, primarily due to the complexities and limitations of existing analytical techniques. In this work, we investigate the relationship between learning dynamics, OOD generalization under covariate shift and the early period of neural network training. We utilize the trace of Fisher Information and sharpness, focusing on gradual unfreezing (i.e., progressively unfreezing parameters during training) as our methodology for investigation. Through a series of empirical experiments, we show that 1) changing the number of trainable parameters during the early period of training via gradual unfreezing can significantly improve OOD results; 2) the trace of Fisher Information and sharpness can be used as indicators for the removal of gradual unfreezing during the early period of training for better OOD generalization. Our experiments on both image and text data show that the early period of training is a general phenomenon that can provide Pareto improvements in ID and OOD performance with minimal complexity. Our work represents a first step towards understanding how early learning dynamics affect neural network OOD generalization under covariate shift and suggests a new avenue to improve and study this problem.