Discriminative Radial Domain Adaptation

• URL: http://arxiv.org/abs/2301.00383v1
• Date: Sun, 1 Jan 2023 10:56:31 GMT
• Title: Discriminative Radial Domain Adaptation
• Authors: Zenan Huang, Jun Wen, Siheng Chen, Linchao Zhu, Nenggan Zheng
• Abstract summary: We propose Discriminative Radial Domain Adaptation (DRDR) which bridges source and target domains via a shared radial structure. We show that transferring such an inherently discriminative structure would enable to enhance feature transferability and discriminability simultaneously. Our method is shown to consistently outperforms state-of-the-art approaches on varied tasks.
• Score: 62.22362756424971
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain adaptation methods reduce domain shift typically by learning domain-invariant features. Most existing methods are built on distribution matching, e.g., adversarial domain adaptation, which tends to corrupt feature discriminability. In this paper, we propose Discriminative Radial Domain Adaptation (DRDR) which bridges source and target domains via a shared radial structure. It's motivated by the observation that as the model is trained to be progressively discriminative, features of different categories expand outwards in different directions, forming a radial structure. We show that transferring such an inherently discriminative structure would enable to enhance feature transferability and discriminability simultaneously. Specifically, we represent each domain with a global anchor and each category a local anchor to form a radial structure and reduce domain shift via structure matching. It consists of two parts, namely isometric transformation to align the structure globally and local refinement to match each category. To enhance the discriminability of the structure, we further encourage samples to cluster close to the corresponding local anchors based on optimal-transport assignment. Extensively experimenting on multiple benchmarks, our method is shown to consistently outperforms state-of-the-art approaches on varied tasks, including the typical unsupervised domain adaptation, multi-source domain adaptation, domain-agnostic learning, and domain generalization.

Related papers

• Making the Best of Both Worlds: A Domain-Oriented Transformer for Unsupervised Domain Adaptation [31.150256154504696]
  Unsupervised Domain Adaptation (UDA) has propelled the deployment of deep learning from limited experimental datasets into real-world unconstrained domains. Most UDA approaches align features within a common embedding space and apply a shared classifier for target prediction. We propose to simultaneously conduct feature alignment in two individual spaces focusing on different domains, and create for each space a domain-oriented classifier.
  arXiv  Detail & Related papers  (2022-08-02T01:38:37Z)
• Birds of A Feather Flock Together: Category-Divergence Guidance for Domain Adaptive Segmentation [35.63920597305474]
  Unsupervised domain adaptation (UDA) aims to enhance the generalization capability of a certain model from a source domain to a target domain. In this work, we propose an Inter-class Separation and Intra-class Aggregation (ISIA) mechanism. By measuring the align complexity of each category, we design an Adaptive-weighted Instance Matching (AIM) strategy to further optimize the instance-level adaptation.
  arXiv  Detail & Related papers  (2022-04-05T11:17:19Z)
• Cross-domain Contrastive Learning for Unsupervised Domain Adaptation [108.63914324182984]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. We build upon contrastive self-supervised learning to align features so as to reduce the domain discrepancy between training and testing sets.
  arXiv  Detail & Related papers  (2021-06-10T06:32:30Z)
• AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection [90.18752912204778]
  Unsupervised domain adaptation for object detection is a challenging problem with many real-world applications. We propose a novel augmented feature alignment network (AFAN) which integrates intermediate domain image generation and domain-adversarial training. Our approach significantly outperforms the state-of-the-art methods on standard benchmarks for both similar and dissimilar domain adaptations.
  arXiv  Detail & Related papers  (2021-06-10T05:01:20Z)
• Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation [74.3349233035632]
  Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) do not consider an inter-class variation within the target domain itself or estimated category. We introduce a learnable clustering module, and a novel domain adaptation framework called cross-domain grouping and alignment. Our method consistently boosts the adaptation performance in semantic segmentation, outperforming the state-of-the-arts on various domain adaptation settings.
  arXiv  Detail & Related papers  (2020-12-15T11:36:21Z)
• Unsupervised Domain Adaptation via Discriminative Manifold Propagation [26.23123292060868]
  Unsupervised domain adaptation is effective in leveraging rich information from a labeled source domain to an unlabeled target domain. The proposed method can be used to tackle a series of variants of domain adaptation problems, including both vanilla and partial settings.
  arXiv  Detail & Related papers  (2020-08-23T12:31:37Z)
• Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation [44.19436340246248]
  This paper presents an innovative local contextual-relation consistent domain adaptation technique. It aims to achieve local-level consistencies during the global-level alignment. Experiments demonstrate its superior segmentation performance as compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-05T19:00:46Z)
• Universal Domain Adaptation through Self Supervision [75.04598763659969]
  Unsupervised domain adaptation methods assume that all source categories are present in the target domain. We propose Domain Adaptative Neighborhood Clustering via Entropy optimization (DANCE) to handle arbitrary category shift. We show through extensive experiments that DANCE outperforms baselines across open-set, open-partial and partial domain adaptation settings.
  arXiv  Detail & Related papers  (2020-02-19T01:26:11Z)
• Bi-Directional Generation for Unsupervised Domain Adaptation [61.73001005378002]
  Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. Conventional methods forcefully reducing the domain discrepancy in the latent space will result in the destruction of intrinsic data structure. We propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains.
  arXiv  Detail & Related papers  (2020-02-12T09:45:39Z)

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.