Feature Protection For Out-of-distribution Generalization

• URL: http://arxiv.org/abs/2405.16027v1
• Date: Sat, 25 May 2024 03:00:06 GMT
• Title: Feature Protection For Out-of-distribution Generalization
• Authors: Lu Tan, Huei Zhou, Yinxiang Huang, Zeming Zheng, Yujiu Yang,
• Abstract summary: We show that protecting pre-trained features leads to a fine-tuned model more robust to generalization. We show that protecting pre-trained features leads to a fine-tuned model more robust to OOD generalization.
• Score: 24.072876186625855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the availability of large pre-trained models, a modern workflow for building real-world machine learning solutions is to fine-tune such models on a downstream task with a relatively small domain-specific dataset. In such applications, one major challenge is that the small fine-tuning dataset does not have sufficient coverage of the distribution encountered when the model is deployed. It is thus important to design fine-tuning methods that are robust to out-of-distribution (OOD) data that are under-represented by the training data. This paper compares common fine-tuning methods to investigate their OOD performance and demonstrates that standard methods will result in a significant change to the pre-trained model so that the fine-tuned features overfit the fine-tuning dataset. However, this causes deteriorated OOD performance. To overcome this issue, we show that protecting pre-trained features leads to a fine-tuned model more robust to OOD generalization. We validate the feature protection methods with extensive experiments of fine-tuning CLIP on ImageNet and DomainNet.

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