Paper:Domain Adaptation via Prompt Learning code: None Abstract: 无监督域适应目标将从源域有标签数据学习到的models适应到无标签的目标域上。目前UDA方法通过对齐源域和目标域特征空间来学习域不变特征。这…
Prompt方法本身具有较好的few-shot, zero-shot学习能力,本文总结了几篇使用Prompt做Domain Adaptation的工作。 Multi-Prompt Alignmentfor Multi-Source Unsupervised Domain Adaptation (arXiv 2023) ADPL: Adversarial Prompt-based Domain Adaptation for Dialogue Summarization with Knowledge Disentanglement (SIGIR 2022) ...
Unsupervised domain adaptation methods aim to facilitate learning tasks in unlabeled target domains using labeled information from related source domains. Recently, prompt-tuning has emerged as a powerful instrument to incorporate templates that reformulate input examples into equivalent cloze-style phrases. ...
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We study test-time domain adaptation for audio deepfake detection (ADD), addressing three challenges: (i) source-target domain gaps, (ii) limited target dataset size, and (iii) high computational costs. We propose an ADD method using prompt tuning in a plug-in style. It bridges domain gaps...
Paper tables with annotated results for Prompt Tuning for Audio Deepfake Detection: Computationally Efficient Test-time Domain Adaptation with Limited Target Dataset
@article{hu2022prosfda, title={ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation}, author={Hu, Shishuai and Liao, Zehui and Xia, Yong}, journal={arXiv preprint arXiv:2211.11514}, year={2022} }...
We further propose a Prompt-based Distribution Alignment (PDA) method to incorporate the domain knowledge into prompt learning. Specifically, PDA employs a two-branch prompt-tuning paradigm, namely base branch and alignment branch. The base branch focuses on integrating class-related representation into...
We study test-time domain adaptation for audio deepfake detection (ADD), addressing three challenges: (i) source-target domain gaps, (ii) limited target dataset size, and (iii) high computational costs. We propose an ADD method using prompt tuning in a plug-in style. It bridges domain gaps...
1、Unsupervised Domain Adaptation 2、Revisiting Prompt Learning Method 1、Prompting for Base Branch 2、Pipeline of Alignment Branch Experiments 1、Experimental Setting 2、Comparisons with Prompt Tuning Methods 3、Comparisons with SOTA Methods 4、Ablation Study 5、Visualization Conclusion Supplementary Ma...