Papers › Enhancing Domain Adaptation through Prompt Gradient Alignment
Enhancing Domain Adaptation through Prompt Gradient Alignment
Hoang Phan, Lam Tran, Quyen Tran, Trung Le
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. In contrast, 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 to align 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 outperforms other vision-language model adaptation methods. The implementation is available at https://github.com/VietHoang1512/PGA.
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Code
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Code Syntology ran Syntology
12 samples harvested; 6 ran; 1 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Adaptation | Office-Home | PGA (ViT-L/14) | Accuracy | 89.4 | #3 of 29 | Archive leaderboard | report |
| Domain Adaptation | Office-Home | PGA (ViT-B/16) | Accuracy | 85.1 | #7 of 29 | Archive leaderboard | report |
| Domain Adaptation | Office-Home | PGA (RN50) | Accuracy | 75.8 | #11 of 29 | Archive leaderboard | report |
| Domain Adaptation | S2RDA-49 | PGA | Accuracy | 74.1 | #1 of 1 | Archive leaderboard | report |
| Domain Adaptation | S2RDA-MS-39 | PGA | Accuracy | 38 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: MPGA, PGA
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