Principled and Efficient Transfer Learning of Deep Models via Neural Collapse
arxiv(2023)
摘要
As model size continues to grow and access to labeled training data remains limited, transfer learning has become a popular approach in many scientific and engineering fields. This study explores the phenomenon of neural collapse (NC) in transfer learning for classification problems, which is characterized by the last-layer features and classifiers of deep networks having zero within-class variability in features and maximally and equally separated between-class feature means. Through the lens of NC, in this work the following findings on transfer learning are discovered: (i) preventing within-class variability collapse to a certain extent during model pre-training on source data leads to better transferability, as it preserves the intrinsic structures of the input data better; (ii) obtaining features with more NC on downstream data during fine-tuning results in better test accuracy. These results provide new insight into commonly used heuristics in model pre-training, such as loss design, data augmentation, and projection heads, and lead to more efficient and principled methods for fine-tuning large pre-trained models. Compared to full model fine-tuning, our proposed fine-tuning methods achieve comparable or even better performance while reducing fine-tuning parameters by at least 70% as well as alleviating overfitting.
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关键词
efficient transfer learning,deep models,neural
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