Provably Uncertainty-Guided Universal Domain Adaptation

• URL: http://arxiv.org/abs/2209.09616v9
• Date: Mon, 30 Sep 2024 20:21:42 GMT
• Title: Provably Uncertainty-Guided Universal Domain Adaptation
• Authors: Yifan Wang, Lin Zhang, Ran Song, Paul L. Rosin, Yibin Li, Wei Zhang,
• Abstract summary: Universal domain adaptation (UniDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain. A main challenge of UniDA is that the nonidentical label sets cause the misalignment between the two domains. We propose a new uncertainty-guided UniDA framework, which exploits the distribution of the target samples in the latent space.
• Score: 34.76381510773768
• License:
• Abstract: Universal domain adaptation (UniDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain without any assumptions of the label sets, which requires distinguishing the unknown samples from the known ones in the target domain. A main challenge of UniDA is that the nonidentical label sets cause the misalignment between the two domains. Moreover, the domain discrepancy and the supervised objectives in the source domain easily lead the whole model to be biased towards the common classes and produce overconfident predictions for unknown samples. To address the above challenging problems, we propose a new uncertainty-guided UniDA framework. Firstly, we introduce an empirical estimation of the probability of a target sample belonging to the unknown class which fully exploits the distribution of the target samples in the latent space. Then, based on the estimation, we propose a novel neighbors searching scheme in a linear subspace with a $\delta$-filter to estimate the uncertainty score of a target sample and discover unknown samples. It fully utilizes the relationship between a target sample and its neighbors in the source domain to avoid the influence of domain misalignment. Secondly, this paper well balances the confidences of predictions for both known and unknown samples through an uncertainty-guided margin loss based on the confidences of discovered unknown samples, which can reduce the gap between the intra-class variances of known classes with respect to the unknown class. Finally, experiments on three public datasets demonstrate that our method significantly outperforms existing state-of-the-art methods.

Related papers

• Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation [22.474866164542302]
  UDA approaches commonly assume that source and target domains share the same labels space. This paper considers the more challenging Source-Free Open-set Domain Adaptation (SF-OSDA) setting. We propose a novel approach for SF-OSDA that exploits the granularity of target-private categories by segregating their samples into multiple unknown classes.
  arXiv  Detail & Related papers  (2024-04-16T13:52:00Z)
• Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment [87.8301166955305]
  We propose a novel bi-discriminator domain adversarial neural network with class-level gradient alignment. BACG resorts to gradient signals and second-order probability estimation for better alignment of domain distributions. In addition, inspired by contrastive learning, we develop a memory bank-based variant, i.e. Fast-BACG, which can greatly shorten the training process.
  arXiv  Detail & Related papers  (2023-10-21T09:53:17Z)
• Heterogeneous Domain Adaptation with Positive and Unlabeled Data [7.48285579561564]
  This paper addresses a new challenging setting called positive and unlabeled heterogeneous unsupervised domain adaptation (PU-HUDA) A naive combination of existing HUDA and PU learning methods is ineffective in PU-HUDA due to the gap in label distribution between the source and target domains. We propose a novel method, predictive adversarial domain adaptation (PADA), which can predict likely positive examples from the unlabeled target data.
  arXiv  Detail & Related papers  (2023-04-17T02:50:18Z)
• Exploiting Inter-Sample Affinity for Knowability-Aware Universal Domain Adaptation [34.5943374866644]
  Universal domain adaptation (UniDA) aims to transfer the knowledge of common classes from the source domain to the target domain without any prior knowledge on the label set. Recent methods usually focused on categorizing a target sample into one of the source classes rather than distinguishing known and unknown samples. We propose a novel UDA framework where such inter-sample affinity is exploited.
  arXiv  Detail & Related papers  (2022-07-19T13:49:30Z)
• Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling [107.24387363079629]
  Domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled or a less labeled but related target domain. Due to the imbalance between the amount of annotated data in the source and target domains, only the target distribution is aligned to the source domain. We propose a non-intrusive Unbiased Transferability Estimation Plug-in (UTEP) by modeling the uncertainty of a discriminator in adversarial-based DA methods to optimize unbiased transfer.
  arXiv  Detail & Related papers  (2022-06-02T21:58:54Z)
• 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)
• Certainty Volume Prediction for Unsupervised Domain Adaptation [35.984559137218504]
  Unsupervised domain adaptation (UDA) deals with the problem of classifying unlabeled target domain data. We propose a novel uncertainty-aware domain adaptation setup that models uncertainty as a multivariate Gaussian distribution in feature space. We evaluate our proposed pipeline on challenging UDA datasets and achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2021-11-03T11:22:55Z)
• OVANet: One-vs-All Network for Universal Domain Adaptation [78.86047802107025]
  Existing methods manually set a threshold to reject unknown samples based on validation or a pre-defined ratio of unknown samples. We propose a method to learn the threshold using source samples and to adapt it to the target domain. Our idea is that a minimum inter-class distance in the source domain should be a good threshold to decide between known or unknown in the target.
  arXiv  Detail & Related papers  (2021-04-07T18:36:31Z)
• Open-Set Hypothesis Transfer with Semantic Consistency [99.83813484934177]
  We introduce a method that focuses on the semantic consistency under transformation of target data. Our model first discovers confident predictions and performs classification with pseudo-labels. As a result, unlabeled data can be classified into discriminative classes coincided with either source classes or unknown classes.
  arXiv  Detail & Related papers  (2020-10-01T10:44:31Z)
• A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation [142.31610972922067]
  This work addresses the unsupervised domain adaptation problem, especially in the case of class labels in the target domain being only a subset of those in the source domain. We build on domain adversarial learning and propose a novel domain adaptation method BA$3$US with two new techniques termed Balanced Adversarial Alignment (BAA) and Adaptive Uncertainty Suppression (AUS) Experimental results on multiple benchmarks demonstrate our BA$3$US surpasses state-of-the-arts for partial domain adaptation tasks.
  arXiv  Detail & Related papers  (2020-03-05T11:37:06Z)

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.