Generalized Universal Domain Adaptation with Generative Flow Networks

• URL: http://arxiv.org/abs/2305.04466v2
• Date: Wed, 30 Aug 2023 03:10:19 GMT
• Title: Generalized Universal Domain Adaptation with Generative Flow Networks
• Authors: Didi Zhu, Yinchuan Li, Yunfeng Shao, Jianye Hao, Fei Wu, Kun Kuang, Jun Xiao, Chao Wu
• Abstract summary: Generalized Universal Domain Adaptation aims to achieve precise prediction of all target labels including unknown categories. GUDA bridges the gap between label distribution shift-based and label space mismatch-based variants. We propose an active domain adaptation algorithm named GFlowDA, which selects diverse samples with probabilities proportional to a reward function.
• Score: 76.1350941965148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a new problem in unsupervised domain adaptation, termed as Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target labels including unknown categories. GUDA bridges the gap between label distribution shift-based and label space mismatch-based variants, essentially categorizing them as a unified problem, guiding to a comprehensive framework for thoroughly solving all the variants. The key challenge of GUDA is developing and identifying novel target categories while estimating the target label distribution. To address this problem, we take advantage of the powerful exploration capability of generative flow networks and propose an active domain adaptation algorithm named GFlowDA, which selects diverse samples with probabilities proportional to a reward function. To enhance the exploration capability and effectively perceive the target label distribution, we tailor the states and rewards, and introduce an efficient solution for parent exploration and state transition. We also propose a training paradigm for GUDA called Generalized Universal Adversarial Network (GUAN), which involves collaborative optimization between GUAN and GFlowNet. Theoretical analysis highlights the importance of exploration, and extensive experiments on benchmark datasets demonstrate the superiority of GFlowDA.

Related papers

• Towards Cross-domain Few-shot Graph Anomaly Detection [6.732699844225434]
  Cross-domain few-shot graph anomaly detection (GAD) is nontrivial owing to inherent data distribution discrepancies between the source and target domains. We propose a simple and effective framework, termed CDFS-GAD, specifically designed to tackle the aforementioned challenges.
  arXiv  Detail & Related papers  (2024-10-11T08:47:25Z)
• Disentangling Masked Autoencoders for Unsupervised Domain Generalization [57.56744870106124]
  Unsupervised domain generalization is fast gaining attention but is still far from well-studied. Disentangled Masked Auto (DisMAE) aims to discover the disentangled representations that faithfully reveal intrinsic features. DisMAE co-trains the asymmetric dual-branch architecture with semantic and lightweight variation encoders.
  arXiv  Detail & Related papers  (2024-07-10T11:11:36Z)
• Inter-Domain Mixup for Semi-Supervised Domain Adaptation [108.40945109477886]
  Semi-supervised domain adaptation (SSDA) aims to bridge source and target domain distributions, with a small number of target labels available. Existing SSDA work fails to make full use of label information from both source and target domains for feature alignment across domains. This paper presents a novel SSDA approach, Inter-domain Mixup with Neighborhood Expansion (IDMNE), to tackle this issue.
  arXiv  Detail & Related papers  (2024-01-21T10:20:46Z)
• Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation [108.40945109477886]
  We propose a novel SSDA approach named Graph-based Adaptive Betweenness Clustering (G-ABC) for achieving categorical domain alignment. Our method outperforms previous state-of-the-art SSDA approaches, demonstrating the superiority of the proposed G-ABC algorithm.
  arXiv  Detail & Related papers  (2024-01-21T09:57:56Z)
• Divide and Adapt: Active Domain Adaptation via Customized Learning [56.79144758380419]
  We present Divide-and-Adapt (DiaNA), a new ADA framework that partitions the target instances into four categories with stratified transferable properties. With a novel data subdivision protocol based on uncertainty and domainness, DiaNA can accurately recognize the most gainful samples. Thanks to the "divideand-adapt" spirit, DiaNA can handle data with large variations of domain gap.
  arXiv  Detail & Related papers  (2023-07-21T14:37:17Z)
• Domain Generalization through the Lens of Angular Invariance [44.76809026901016]
  Domain generalization (DG) aims at generalizing a classifier trained on multiple source domains to an unseen target domain with domain shift. We propose a novel deep DG method called Angular Invariance Domain Generalization Network (AIDGN)
  arXiv  Detail & Related papers  (2022-10-28T02:05:38Z)
• Label-Efficient Domain Generalization via Collaborative Exploration and Generalization [28.573872986524794]
  This paper introduces label-efficient domain generalization (LEDG) to enable model generalization with label-limited source domains. We propose a novel framework called Collaborative Exploration and Generalization (CEG) which jointly optimize active exploration and semi-supervised generalization.
  arXiv  Detail & Related papers  (2022-08-07T05:34:50Z)
• Deep Domain Generalization with Feature-norm Network [33.84004077585957]
  We introduce an end-to-end feature-norm network (FNN) which is robust to negative transfer. We also introduce a collaborative feature-norm network (CFNN) to further improve the capability of FNN.
  arXiv  Detail & Related papers  (2021-04-28T06:13:47Z)
• 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)
• Unveiling Class-Labeling Structure for Universal Domain Adaptation [12.411096265140479]
  We employ a probabilistic approach for locating the common label set, where each source class may come from the common label set with a probability. We propose a simple universal adaptation network (S-UAN) by incorporating the probabilistic structure for the common label set. Experiments indicate that S-UAN works well in different UDA settings and outperforms the state-of-the-art methods by large margins.
  arXiv  Detail & Related papers  (2020-10-10T02:13:02Z)

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.