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。这种对齐是由诸如统计差异最小化或对抗性训练等约束施加的。然而这些约束可能会导致语义特征结构的扭曲和类可辨...
[2] ADPL: Adversarial Prompt-based Domain Adaptation for Dialogue Summarization with Knowledge Disentanglement (SIGIR 2022) 本文提出了对抗解耦提示学习(Adversarial Disentangled Prompt Learning, ADPL)用于对话摘要中的领域适应任务。其中有3种Prompt,分别是域不变Prompt (DIP)、域特定Prompt (DSP)和面向任务Promp...
传统的Domain Adaptation方法主要分为三类：数据分布对齐、特征空间对齐和对抗性训练。这些方法在一定程度上...
Prompt Learning with Cross-Modal Feature Alignment for Visual Domain AdaptationExploring the capacity of pre-trained large-scale models to learn common features of multimodal data and the effect of knowledge transfer on downstream tasks are two major trends in the multimedia field. However, existing ...
Finally, to complement insufficient transferred information, anAdaptive Promptis learned to incorporate additional target characteristics for model adaptation. Consequently, the collaboration of these three types of prompts contributes to a hybridly prompted model that achieves domain-aware encoding, transfer,...
Domain adaptation and continual learning in semantic segmentation Person re-identification (ReID) has made good progress in stationary domains. The ReID model must be retrained to adapt to new scenarios (domains) as they ... U Michieli,M Toldo,P Zanuttigh - 《Advanced Methods & Deep Learning...
A Survey on Deep Domain Adaptation for LiDAR Perception [7 Jun 2021] A Comprehensive Survey on Transfer Learning [7 Nov 2019] Transfer Adaptation Learning: A Decade Survey [12 Mar 2019] A review of single-source unsupervised domain adaptation [16 Jan 2019] An introduction to domain adaptation ...
Transfer Adaptation Learning: A Decade Survey [12 Mar 2019] A review of single-source unsupervised domain adaptation [16 Jan 2019] An introduction to domain adaptation and transfer learning [31 Dec 2018] A Survey of Unsupervised Deep Domain Adaptation [6 Dec 2018] Transfer Learning for Cross-Dat...
1、Unsupervised Domain Adaptation 2、Vision Language Models 3、Prompt Tuning in Vision Language Models Preliminaries 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 ...
2、freeze the pre-trained model and only tune the input data (e.g., prompt) for model adaptation(冻结网络只调整少量参数) Motivation for work:learn transferable (domain-agnostic) prompts to effectively leverage both pre-trained knowledge and source-knowledge for the target domain using CLIP. 文...