Guidance Not Obstruction: A Conjugate Consistent Enhanced Strategy for Domain Generalization

• URL: http://arxiv.org/abs/2412.10089v1
• Date: Fri, 13 Dec 2024 12:25:16 GMT
• Title: Guidance Not Obstruction: A Conjugate Consistent Enhanced Strategy for Domain Generalization
• Authors: Meng Cao, Songcan Chen,
• Abstract summary: We argue that acquiring discriminative generalization between classes within domains is crucial. In contrast to seeking distribution alignment, we endeavor to safeguard domain-related between-class discrimination. We employ a novel distribution-level Universum strategy to generate supplementary diverse domain-related class-conditional distributions.
• Score: 50.04665252665413
• License:
• Abstract: Domain generalization addresses domain shift in real-world applications. Most approaches adopt a domain angle, seeking invariant representation across domains by aligning their marginal distributions, irrespective of individual classes, naturally leading to insufficient exploration of discriminative information. Switching to a class angle, we find that multiple domain-related peaks or clusters within the same individual classes must emerge due to distribution shift. In other words, marginal alignment does not guarantee conditional alignment, leading to suboptimal generalization. Therefore, we argue that acquiring discriminative generalization between classes within domains is crucial. In contrast to seeking distribution alignment, we endeavor to safeguard domain-related between-class discrimination. To this end, we devise a novel Conjugate Consistent Enhanced Module, namely Con2EM, based on a distribution over domains, i.e., a meta-distribution. Specifically, we employ a novel distribution-level Universum strategy to generate supplementary diverse domain-related class-conditional distributions, thereby enhancing generalization. This allows us to resample from these generated distributions to provide feedback to the primordial instance-level classifier, further improving its adaptability to the target-agnostic. To ensure generation accuracy, we establish an additional distribution-level classifier to regularize these conditional distributions. Extensive experiments have been conducted to demonstrate its effectiveness and low computational cost compared to SOTAs.

Related papers

• Discriminative Radial Domain Adaptation [62.22362756424971]
  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.
  arXiv  Detail & Related papers  (2023-01-01T10:56:31Z)
• Constrained Maximum Cross-Domain Likelihood for Domain Generalization [14.91361835243516]
  Domain generalization aims to learn a generalizable model on multiple source domains, which is expected to perform well on unseen test domains. In this paper, we propose a novel domain generalization method, which minimizes the KL-divergence between posterior distributions from different domains. Experiments on four standard benchmark datasets, i.e., Digits-DG, PACS, Office-Home and miniDomainNet, highlight the superior performance of our method.
  arXiv  Detail & Related papers  (2022-10-09T03:41:02Z)
• Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge [22.285156929279207]
  Domain generalization aims at learning a universal model that performs well on unseen target domains. We propose a novel domain generalization framework called Wasserstein Distributionally Robust Domain Generalization (WDRDG)
  arXiv  Detail & Related papers  (2022-07-11T14:46:50Z)
• Localized Adversarial Domain Generalization [83.4195658745378]
  Adversarial domain generalization is a popular approach to domain generalization. We propose localized adversarial domain generalization with space compactness maintenance(LADG) We conduct comprehensive experiments on the Wilds DG benchmark to validate our approach.
  arXiv  Detail & Related papers  (2022-05-09T08:30:31Z)
• Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimization [12.10885662305154]
  Given multiple source domains, domain generalization aims at learning a universal model that performs well on any unseen but related target domain. Existing approaches are not sufficiently robust when the variation of conditional distributions given the same class is large. We extend the concept of distributional robust optimization to solve the class-conditional domain generalization problem.
  arXiv  Detail & Related papers  (2021-09-08T14:23:03Z)
• Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalization [33.84004077585957]
  We propose a discriminative domain-invariant adversarial network (DDIAN) for domain generalization. DDIAN achieves better prediction on unseen target data during training compared to state-of-the-art domain generalization approaches.
  arXiv  Detail & Related papers  (2021-08-20T04:24:12Z)
• Generalizable Representation Learning for Mixture Domain Face Anti-Spoofing [53.82826073959756]
  Face anti-spoofing approach based on domain generalization(DG) has drawn growing attention due to its robustness forunseen scenarios. We propose domain dy-namic adjustment meta-learning (D2AM) without using do-main labels. To overcome the limitation, we propose domain dy-namic adjustment meta-learning (D2AM) without using do-main labels.
  arXiv  Detail & Related papers  (2021-05-06T06:04:59Z)
• 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)
• Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation [109.73983088432364]
  We propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA) We introduce the LIRR algorithm for jointly textbfLearning textbfInvariant textbfRepresentations and textbfRisks.
  arXiv  Detail & Related papers  (2020-10-09T15:42:35Z)
• Class Distribution Alignment for Adversarial Domain Adaptation [32.95056492475652]
  Conditional ADversarial Image Translation (CADIT) is proposed to explicitly align the class distributions given samples between the two domains. It integrates a discriminative structure-preserving loss and a joint adversarial generation loss. Our approach achieves superior classification in the target domain when compared to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-04-20T15:58:11Z)

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.