Multi-source Attention for Unsupervised Domain Adaptation

• URL: http://arxiv.org/abs/2004.06608v2
• Date: Fri, 17 Apr 2020 13:48:36 GMT
• Title: Multi-source Attention for Unsupervised Domain Adaptation
• Authors: Xia Cui and Danushka Bollegala
• Abstract summary: 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. We then learn attention-weights over the sources for aggregating the predictions of the source-specific models.
• Score: 15.900069711477542
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Noisy Universal Domain Adaptation via Divergence Optimization for Visual Recognition [30.31153237003218]
  A novel scenario named Noisy UniDA is proposed to transfer knowledge from a labeled source domain to an unlabeled target domain. A multi-head convolutional neural network framework is proposed to address all of the challenges faced in the Noisy UniDA at once.
  arXiv  Detail & Related papers  (2023-04-20T14:18:38Z)
• Source-Free Domain Adaptation via Distribution Estimation [106.48277721860036]
  Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data. In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation.
  arXiv  Detail & Related papers  (2022-04-24T12:22:19Z)
• Multi-Source domain adaptation via supervised contrastive learning and confident consistency regularization [0.0]
  Multi-Source Unsupervised Domain Adaptation (multi-source UDA) aims to learn a model from several labeled source domains. We propose Contrastive Multi-Source Domain Adaptation (CMSDA) for multi-source UDA that addresses this limitation.
  arXiv  Detail & Related papers  (2021-06-30T14:39:15Z)
• Aggregating From Multiple Target-Shifted Sources [7.644958631142882]
  Multi-source domain adaptation aims at leveraging the knowledge from multiple tasks for predicting a related target domain. In this paper, we analyzed the problem for aggregating source domains with different label distributions, where most recent source selection approaches fail. Our proposed algorithm differs from previous approaches in two key ways: the model aggregates multiple sources mainly through the similarity of semantic conditional distribution rather than marginal distribution.
  arXiv  Detail & Related papers  (2021-05-09T23:25:29Z)
• Unsupervised Multi-source Domain Adaptation Without Access to Source Data [58.551861130011886]
  Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an unlabeled domain by transferring knowledge from a separate labeled source domain. We propose a novel and efficient algorithm which automatically combines the source models with suitable weights in such a way that it performs at least as good as the best source model.
  arXiv  Detail & Related papers  (2021-04-05T10:45:12Z)
• Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation [74.71931918541748]
  We propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-03T01:33:14Z)
• Mutual Learning Network for Multi-Source Domain Adaptation [73.25974539191553]
  We propose a novel multi-source domain adaptation method, Mutual Learning Network for Multiple Source Domain Adaptation (ML-MSDA) Under the framework of mutual learning, the proposed method pairs the target domain with each single source domain to train a conditional adversarial domain adaptation network as a branch network. The proposed method outperforms the comparison methods and achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-03-29T04:31:43Z)
• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation [102.67010690592011]
  Unsupervised adaptationUDA (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model. This work tackles a practical setting where only a trained source model is available and how we can effectively utilize such a model without source data to solve UDA problems.
  arXiv  Detail & Related papers  (2020-02-20T03:13:58Z)
• Multi-source Domain Adaptation for Visual Sentiment Classification [92.53780541232773]
  We propose a novel multi-source domain adaptation (MDA) method, termed Multi-source Sentiment Generative Adversarial Network (MSGAN) To handle data from multiple source domains, MSGAN learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. Extensive experiments conducted on four benchmark datasets demonstrate that MSGAN significantly outperforms the state-of-the-art MDA approaches for visual sentiment classification.
  arXiv  Detail & Related papers  (2020-01-12T08:37:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.