Summary of Enhancing Domain Adaptation Through Prompt Gradient Alignment, by Hoang Phan et al.
Enhancing Domain Adaptation through Prompt Gradient Alignment
by Hoang Phan, Lam Tran, Quyen Tran, Trung Le
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new approach to Prior Unsupervised Domain Adaptation (UDA) by casting it as a multiple-objective optimization problem. This framework allows for the learning of both domain-invariant and specific features through learnable prompts. The proposed method aligns per-objective gradients to foster consensus between them, while also penalizing their norm to prevent overfitting during fine-tuning. Experimental results show that this approach consistently outperforms prompt-based baselines on various UDA benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers learn new things even when they’re trained on different kinds of data. Usually, this kind of learning is hard because the computer gets stuck thinking everything looks the same. To solve this problem, some researchers have used special prompts to help the computer learn both general and specific things at once. This paper takes a different approach by treating UDA as a special math problem with multiple goals. By aligning these goals, the computer can learn what’s common between them and figure out how to adapt to new situations. The authors also come up with a way to prevent the computer from getting too good at learning one thing and forgetting everything else. They test their idea on some benchmarks and find that it works really well. |
Keywords
» Artificial intelligence » Domain adaptation » Fine tuning » Optimization » Overfitting » Prompt » Unsupervised
