Debiased Fine-Tuning for Vision-language Models by Prompt Regularization

• URL: http://arxiv.org/abs/2301.12429v2
• Date: Fri, 31 Mar 2023 07:05:35 GMT
• Title: Debiased Fine-Tuning for Vision-language Models by Prompt Regularization
• Authors: Beier Zhu and Yulei Niu and Saeil Lee and Minhoe Hur and Hanwang Zhang
• Abstract summary: We present a new paradigm for fine-tuning large-scale vision pre-trained models on downstream task, dubbed Prompt Regularization (ProReg) ProReg uses the prediction by prompting the pretrained model to regularize the fine-tuning. We show the consistently strong performance of ProReg compared with conventional fine-tuning, zero-shot prompt, prompt tuning, and other state-of-the-art methods.
• Score: 50.41984119504716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a new paradigm for fine-tuning large-scale visionlanguage pre-trained models on downstream task, dubbed Prompt Regularization (ProReg). Different from traditional fine-tuning which easily overfits to the downstream task data, ProReg uses the prediction by prompting the pretrained model to regularize the fine-tuning. The motivation is: by prompting the large model "a photo of a [CLASS]", the fil-lin answer is only dependent on the pretraining encyclopedic knowledge while independent of the task data distribution, which is usually biased. Specifically, given a training sample prediction during fine-tuning, we first calculate its KullbackLeibler loss of the prompt prediction and Cross-Entropy loss of the ground-truth label, and then combine them with a proposed sample-wise adaptive trade-off weight, which automatically adjusts the transfer between the pretrained and downstream domains. On various out-of-distribution benchmarks, we show the consistently strong performance of ProReg compared with conventional fine-tuning, zero-shot prompt, prompt tuning, and other state-of-the-art methods.

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