On consequences of finetuning on data with highly discriminative features
Machine Learning
2023-11-17 v2
Abstract
In the era of transfer learning, training neural networks from scratch is becoming obsolete. Transfer learning leverages prior knowledge for new tasks, conserving computational resources. While its advantages are well-documented, we uncover a notable drawback: networks tend to prioritize basic data patterns, forsaking valuable pre-learned features. We term this behavior "feature erosion" and analyze its impact on network performance and internal representations.
Keywords
Cite
@article{arxiv.2310.19537,
title = {On consequences of finetuning on data with highly discriminative features},
author = {Wojciech Masarczyk and Tomasz Trzciński and Mateusz Ostaszewski},
journal= {arXiv preprint arXiv:2310.19537},
year = {2023}
}
Comments
NeurIPS 2023 -- UniReps Workshop