Dual-Path Stable Soft Prompt Generation for Domain Generalization

• URL: http://arxiv.org/abs/2505.18770v1
• Date: Sat, 24 May 2025 16:20:06 GMT
• Title: Dual-Path Stable Soft Prompt Generation for Domain Generalization
• Abstract summary: Domain generalization (DG) aims to learn a model using data from one or multiple related but distinct source domains.<n> prompt tuning has emerged as an effective generalization strategy.<n>It often struggles to capture domain-specific features due to its reliance on manually or fixed prompt inputs.<n>Recent prompt generation methods have addressed this limitation by dynamically generating instance-specific and domain-specific prompts.
• Score: 13.957351735394683
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain generalization (DG) aims to learn a model using data from one or multiple related but distinct source domains that can generalize well to unseen out-of-distribution target domains. Inspired by the success of large pre-trained vision-language models (VLMs), prompt tuning has emerged as an effective generalization strategy. However, it often struggles to capture domain-specific features due to its reliance on manually or fixed prompt inputs. Recently, some prompt generation methods have addressed this limitation by dynamically generating instance-specific and domain-specific prompts for each input, enriching domain information and demonstrating potential for enhanced generalization. Through further investigation, we identify a notable issue in existing prompt generation methods: the same input often yields significantly different and suboptimal prompts across different random seeds, a phenomenon we term Prompt Variability. To address this, we introduce negative learning into the prompt generation process and propose Dual-Path Stable Soft Prompt Generation (DPSPG), a transformer-based framework designed to improve both the stability and generalization of prompts. Specifically, DPSPG incorporates a complementary prompt generator to produce negative prompts, thereby reducing the risk of introducing misleading information. Both theoretical and empirical analyses demonstrate that negative learning leads to more robust and effective prompts by increasing the effective margin and reducing the upper bound of the gradient norm. Extensive experiments on five DG benchmark datasets show that DPSPG consistently outperforms state-of-the-art methods while maintaining prompt stability.

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