Early Period of Training Impacts Out-of-Distribution Generalization

• URL: http://arxiv.org/abs/2403.15210v1
• Date: Fri, 22 Mar 2024 13:52:53 GMT
• Title: Early Period of Training Impacts Out-of-Distribution Generalization
• Authors: Chen Cecilia Liu, Iryna Gurevych,
• Abstract summary: We investigate the relationship between learning dynamics and OOD generalization during the early period of neural network training. We show that selecting the number of trainable parameters at different times during training has a minuscule impact on ID results. The absolute values of sharpness and trace of Fisher Information at the initial period of training are not indicative for OOD generalization.
• Score: 56.283944756315066
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Prior research has found that differences in the early period of neural network training significantly impact the performance of in-distribution (ID) tasks. However, neural networks are often sensitive to out-of-distribution (OOD) data, making them less reliable in downstream applications. Yet, the impact of the early training period on OOD generalization remains understudied due to its complexity and lack of effective analytical methodologies. In this work, we investigate the relationship between learning dynamics and OOD generalization during the early period of neural network training. We utilize the trace of Fisher Information and sharpness, with a focus on gradual unfreezing (i.e. progressively unfreezing parameters during training) as the methodology for investigation. Through a series of empirical experiments, we show that 1) selecting the number of trainable parameters at different times during training, i.e. realized by gradual unfreezing -- has a minuscule impact on ID results, but greatly affects the generalization to OOD data; 2) the absolute values of sharpness and trace of Fisher Information at the initial period of training are not indicative for OOD generalization, but the relative values could be; 3) the trace of Fisher Information and sharpness may be used as indicators for the removal of interventions during early period of training for better OOD generalization.

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