More is Better: Deep Domain Adaptation with Multiple Sources

• URL: http://arxiv.org/abs/2405.00749v1
• Date: Wed, 1 May 2024 03:37:12 GMT
• Title: More is Better: Deep Domain Adaptation with Multiple Sources
• Authors: Sicheng Zhao, Hui Chen, Hu Huang, Pengfei Xu, Guiguang Ding,
• Abstract summary: Multi-source domain adaptation (MDA) is a powerful and practical extension in which the labeled data may be collected from multiple sources with different distributions. In this survey, we first define various MDA strategies. Then we systematically summarize and compare modern MDA methods in the deep learning era from different perspectives.
• Score: 34.26271755493111
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In many practical applications, it is often difficult and expensive to obtain large-scale labeled data to train state-of-the-art deep neural networks. Therefore, transferring the learned knowledge from a separate, labeled source domain to an unlabeled or sparsely labeled target domain becomes an appealing alternative. However, direct transfer often results in significant performance decay due to domain shift. Domain adaptation (DA) aims to address this problem by aligning the distributions between the source and target domains. Multi-source domain adaptation (MDA) is a powerful and practical extension in which the labeled data may be collected from multiple sources with different distributions. In this survey, we first define various MDA strategies. Then we systematically summarize and compare modern MDA methods in the deep learning era from different perspectives, followed by commonly used datasets and a brief benchmark. Finally, we discuss future research directions for MDA that are worth investigating.

Related papers

• Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacher [11.616494893839757]
  Adapting visual object detectors to operational target domains is a challenging task, commonly achieved using unsupervised domain adaptation (UDA) methods. Recent studies have shown that when the labeled dataset comes from multiple source domains, treating them as separate domains improves the accuracy and robustness over blending these source domains and performing a UDA. This paper proposes a novel MSDA method called Prototype-based Mean Teacher (PMT), which uses class prototypes instead of domain-specifics to encode domain-specific information.
  arXiv  Detail & Related papers  (2023-09-26T14:08:03Z)
• Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation [51.15061013818216]
  Supervised approaches have led to great success with the advance of deep learning, but they rely on large quantities of ground-truth depth annotations. Unsupervised domain adaptation (UDA) transfers knowledge from labeled source data to unlabeled target data, so as to relax the constraint of supervised learning. We propose a novel UDA method for MDE, referred to as Learning Feature Decomposition for Adaptation (LFDA), which learns to decompose the feature space into content and style components.
  arXiv  Detail & Related papers  (2022-07-30T08:05:35Z)
• Multi-Source Unsupervised Domain Adaptation via Pseudo Target Domain [0.0]
  Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple source domains to an unlabeled target domain. We propose a novel MDA approach, termed Pseudo Target for MDA (PTMDA) PTMDA maps each group of source and target domains into a group-specific subspace using adversarial learning with a metric constraint. We show that PTMDA as a whole can reduce the target error bound and leads to a better approximation of the target risk in MDA settings.
  arXiv  Detail & Related papers  (2022-02-22T08:37:16Z)
• Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layers [1.3766148734487902]
  Multi-source unsupervised domain adaptation (MSDA) aims at learning a predictor for an unlabeled domain by assigning weak knowledge from a bag of source models. We propose to embed Multi-Source version of DomaIn Alignment Layers (MS-DIAL) at different levels of the predictor. Our approach can improve state-of-the-art MSDA methods, yielding relative gains of up to +30.64% on their classification accuracies.
  arXiv  Detail & Related papers  (2021-09-06T18:41:19Z)
• A Review of Single-Source Deep Unsupervised Visual Domain Adaptation [81.07994783143533]
  Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. In many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
  arXiv  Detail & Related papers  (2020-09-01T00:06:50Z)
• Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation [65.38975706997088]
  Open set domain adaptation (OSDA) assumes the presence of unknown classes in the target domain. We show that existing state-of-the-art methods suffer a considerable performance drop in the presence of larger domain gaps. We propose a novel framework to specifically address the larger domain gaps.
  arXiv  Detail & Related papers  (2020-03-08T14:20:24Z)
• Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey [53.656086832255944]
  Multi-source domain adaptation (MDA) is a powerful extension in which the labeled data may be collected from multiple sources. MDA has attracted increasing attention in both academia and industry.
  arXiv  Detail & Related papers  (2020-02-26T08:07:58Z)
• MADAN: Multi-source Adversarial Domain Aggregation Network for Domain Adaptation [58.38749495295393]
  Domain adaptation aims to learn a transferable model to bridge the domain shift between one labeled source domain and another sparsely labeled or unlabeled target domain. Recent multi-source domain adaptation (MDA) methods do not consider the pixel-level alignment between sources and target. We propose a novel MDA framework to address these challenges.
  arXiv  Detail & Related papers  (2020-02-19T21:22:00Z)
• 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.