Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot Learning

• URL: http://arxiv.org/abs/2411.10070v1
• Date: Fri, 15 Nov 2024 09:34:07 GMT
• Title: Step-wise Distribution Alignment Guided Style Prompt Tuning for Source-free Cross-domain Few-shot Learning
• Authors: Huali Xu, Yongxiang Liu, Li Liu, Shuaifeng Zhi, Shuzhou Sun, Tianpeng Liu, MingMing Cheng,
• Abstract summary: Cross-domain few-shot learning methods face challenges with large-scale pre-trained models due to inaccessible source data and training strategies. This paper introduces Step-wise Distribution Alignment Guided Style Prompt Tuning (StepSPT) StepSPT implicitly narrows domain gaps through prediction distribution optimization.
• Score: 53.60934432718044
• License:
• Abstract: Existing cross-domain few-shot learning (CDFSL) methods, which develop source-domain training strategies to enhance model transferability, face challenges with large-scale pre-trained models (LMs) due to inaccessible source data and training strategies. Moreover, fine-tuning LMs for CDFSL demands substantial computational resources, limiting practicality. This paper addresses the source-free CDFSL (SF-CDFSL) problem, tackling few-shot learning (FSL) in the target domain using only pre-trained models and a few target samples without source data or strategies. To overcome the challenge of inaccessible source data, this paper introduces Step-wise Distribution Alignment Guided Style Prompt Tuning (StepSPT), which implicitly narrows domain gaps through prediction distribution optimization. StepSPT proposes a style prompt to align target samples with the desired distribution and adopts a dual-phase optimization process. In the external process, a step-wise distribution alignment strategy factorizes prediction distribution optimization into a multi-step alignment problem to tune the style prompt. In the internal process, the classifier is updated using standard cross-entropy loss. Evaluations on five datasets demonstrate that StepSPT outperforms existing prompt tuning-based methods and SOTAs. Ablation studies further verify its effectiveness. Code will be made publicly available at \url{https://github.com/xuhuali-mxj/StepSPT}.

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