Enhancing Domain Adaptation through Prompt Gradient Alignment

• URL: http://arxiv.org/abs/2406.09353v2
• Date: Sun, 27 Oct 2024 23:40:32 GMT
• Title: Enhancing Domain Adaptation through Prompt Gradient Alignment
• Authors: Hoang Phan, Lam Tran, Quyen Tran, Trung Le,
• Abstract summary: We develop a line of works based on prompt learning to learn both domain-invariant and specific features. We cast UDA as a multiple-objective optimization problem in which each objective is represented by a domain loss. Our method consistently surpasses other prompt-based baselines by a large margin on different UDA benchmarks.
• Score: 16.618313165111793
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Prior Unsupervised Domain Adaptation (UDA) methods often aim to train a domain-invariant feature extractor, which may hinder the model from learning sufficiently discriminative features. To tackle this, a line of works based on prompt learning leverages the power of large-scale pre-trained vision-language models to learn both domain-invariant and specific features through a set of domain-agnostic and domain-specific learnable prompts. Those studies typically enforce invariant constraints on representation, output, or prompt space to learn such prompts. Differently, we cast UDA as a multiple-objective optimization problem in which each objective is represented by a domain loss. Under this new framework, we propose aligning per-objective gradients to foster consensus between them. Additionally, to prevent potential overfitting when fine-tuning this deep learning architecture, we penalize the norm of these gradients. To achieve these goals, we devise a practical gradient update procedure that can work under both single-source and multi-source UDA. Empirically, our method consistently surpasses other prompt-based baselines by a large margin on different UDA benchmarks.

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