Multi-source Attention for Unsupervised Domain Adaptation

2020-04-14 · Asian Chapter of the Association for Computational Linguistics 2020 · Xia Cui, Danushka Bollegala

Domain adaptation considers the problem of generalising a model learnt using data from a particular source domain to a different target domain. Often it is difficult to find a suitable single source to adapt from, and one must consider multiple sources. Using an unrelated source can result in sub-optimal performance, known as the \emph{negative transfer}. However, it is challenging to select the appropriate source(s) for classifying a given target instance in multi-source unsupervised domain adaptation (UDA). We model source-selection as an attention-learning problem, where we learn attention over sources for a given target instance. For this purpose, we first independently learn source-specific classification models, and a relatedness map between sources and target domains using pseudo-labelled target domain instances. Next, we learn attention-weights over the sources for aggregating the predictions of the source-specific models. Experimental results on cross-domain sentiment classification benchmarks show that the proposed method outperforms prior proposals in multi-source UDA.

📄 PDF Abstract BibTeX arXiv:2004.06608

Code (1)

summer1278/multi-source-attention 공식 구현 pytorch

Tasks

Domain Adaptation General ClassificationMulti-Source Unsupervised Domain AdaptationSentiment AnalysisSentiment ClassificationUnsupervised Domain Adaptation

Joint Attention-Driven Domain Fusion and Noise-Tolerant Learning for Multi-Source Domain Adaptation

2022-08-05 · Tong Xu, Lin Wang, Wu Ning, Chunyan Lyu 외

As a study on the efficient usage of data, Multi-source Unsupervised Domain Adaptation transfers knowledge from multiple source domains with labeled data to an unlabeled target domain. However, the distribution discrepan…

Domain Adaptation Multi-Source Unsupervised Domain Adaptation Unsupervised Domain Adaptation

Attention-based Cross-Layer Domain Alignment for Unsupervised Domain Adaptation

2022-02-27 · Xu Ma, Junkun Yuan, Yen-Wei Chen, Ruofeng Tong 외

Unsupervised domain adaptation (UDA) aims to learn transferable knowledge from a labeled source domain and adapts a trained model to an unlabeled target domain. To bridge the gap between source and target domains, one pr…

Domain Adaptation Semantic Similarity Semantic Textual Similarity Unsupervised Domain Adaptation

One-Shot Federated Unsupervised Domain Adaptation with Scaled Entropy Attention and Multi-Source Smoothed Pseudo Labeling

2025-03-13 · Ali Abedi, Q. M. Jonathan Wu, Ning Zhang, Farhad Pourpanah

Federated Learning (FL) is a promising approach for privacy-preserving collaborative learning. However, it faces significant challenges when dealing with domain shifts, especially when each client has access only to its …

Domain Adaptation Federated Learning Privacy Preserving Unsupervised Domain Adaptation

Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data

2021-07-14 · Ning Ma, Jiajun Bu, Zhen Zhang, Sheng Zhou

Present domain adaptation methods usually perform explicit representation alignment by simultaneously accessing the source data and target data. However, the source data are not always available due to the privacy preser…

Domain Adaptation Privacy Preserving Semi-supervised Domain Adaptation Source-Free Domain Adaptation

Spatial Attention Pyramid Network for Unsupervised Domain Adaptation

2020-03-29 · ECCV 2020 8 · Cong-Cong Li, Dawei Du, Libo Zhang, Longyin Wen 외

Unsupervised domain adaptation is critical in various computer vision tasks, such as object detection, instance segmentation, and semantic segmentation, which aims to alleviate performance degradation caused by domain-sh…

Domain Adaptation Instance Segmentation object-detection Object Detection+3