When Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approach

• URL: http://arxiv.org/abs/2503.14897v2
• Date: Fri, 21 Mar 2025 14:15:36 GMT
• Title: When Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approach
• Authors: Vaibhav Rathore, Shubhranil B, Saikat Dutta, Sarthak Mehrotra, Zsolt Kira, Biplab Banerjee,
• Abstract summary: Generalized Class Discovery (GCD) clusters base and novel classes in a target domain using supervision from a source domain with only base classes.<n>We introduce the novel paradigm of Domain Generalization in GCD (DG-GCD), where only source data is available for training.<n>Our solution, DG2CD-Net, aims to construct a domain-independent, discriminative embedding space for GCD.
• Score: 29.664356718958803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generalized Class Discovery (GCD) clusters base and novel classes in a target domain using supervision from a source domain with only base classes. Current methods often falter with distribution shifts and typically require access to target data during training, which can sometimes be impractical. To address this issue, we introduce the novel paradigm of Domain Generalization in GCD (DG-GCD), where only source data is available for training, while the target domain, with a distinct data distribution, remains unseen until inference. To this end, our solution, DG2CD-Net, aims to construct a domain-independent, discriminative embedding space for GCD. The core innovation is an episodic training strategy that enhances cross-domain generalization by adapting a base model on tasks derived from source and synthetic domains generated by a foundation model. Each episode focuses on a cross-domain GCD task, diversifying task setups over episodes and combining open-set domain adaptation with a novel margin loss and representation learning for optimizing the feature space progressively. To capture the effects of fine-tuning on the base model, we extend task arithmetic by adaptively weighting the local task vectors concerning the fine-tuned models based on their GCD performance on a validation distribution. This episodic update mechanism boosts the adaptability of the base model to unseen targets. Experiments across three datasets confirm that DG2CD-Net outperforms existing GCD methods customized for DG-GCD.

Related papers

• Decentralized Federated Domain Generalization with Style Sharing: A Formal Modeling and Convergence Analysis [19.571001440750234]
  This paper is motivated by the lack of formal mathematical analysis of domain generalization objectives and training processes. We develop a fully decentralized DG algorithm to allow devices in a peer-to-peer network to achieve DG based on sharing style information inferred from their datasets. We show that $textttStyleDDG$ can obtain significant improvements in accuracy across target domains with minimal added communication overhead.
  arXiv  Detail & Related papers  (2025-04-08T17:32:56Z)
• Generative Classifier for Domain Generalization [84.92088101715116]
  Domain generalization aims to the generalizability of computer vision models toward distribution shifts. We propose Generative-driven Domain Generalization (GCDG) GCDG consists of three key modules: Heterogeneity Learning(HLC), Spurious Correlation(SCB), and Diverse Component Balancing(DCB)
  arXiv  Detail & Related papers  (2025-04-03T04:38:33Z)
• Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler [45.71475375161575]
  In Open-Set Domain Generalization, the model is exposed to both new variations of data appearance (domains) and open-set conditions. We propose the Evidential Bi-Level Hardest Domain Scheduler (EBiL-HaDS) to achieve an adaptive domain scheduler.
  arXiv  Detail & Related papers  (2024-09-26T05:57:35Z)
• CNN Feature Map Augmentation for Single-Source Domain Generalization [6.053629733936548]
  Domain Generalization (DG) has gained significant traction during the past few years. The goal in DG is to produce models which continue to perform well when presented with data distributions different from the ones available during training. We propose an alternative regularization technique for convolutional neural network architectures in the single-source DG image classification setting.
  arXiv  Detail & Related papers  (2023-05-26T08:48:17Z)
• Upcycling Models under Domain and Category Shift [95.22147885947732]
  We introduce an innovative global and local clustering learning technique (GLC) We design a novel, adaptive one-vs-all global clustering algorithm to achieve the distinction across different target classes. Remarkably, in the most challenging open-partial-set DA scenario, GLC outperforms UMAD by 14.8% on the VisDA benchmark.
  arXiv  Detail & Related papers  (2023-03-13T13:44:04Z)
• Polycentric Clustering and Structural Regularization for Source-free Unsupervised Domain Adaptation [20.952542421577487]
  Source-Free Domain Adaptation (SFDA) aims to solve the domain adaptation problem by transferring the knowledge learned from a pre-trained source model to an unseen target domain. Most existing methods assign pseudo-labels to the target data by generating feature prototypes. In this paper, a novel framework named PCSR is proposed to tackle SFDA via a novel intra-class Polycentric Clustering and Structural Regularization strategy.
  arXiv  Detail & Related papers  (2022-10-14T02:20:48Z)
• Adaptive Domain Generalization via Online Disagreement Minimization [17.215683606365445]
  Domain Generalization aims to safely transfer a model to unseen target domains. AdaODM adaptively modifies the source model at test time for different target domains. Results show AdaODM stably improves the generalization capacity on unseen domains.
  arXiv  Detail & Related papers  (2022-08-03T11:51:11Z)
• Deep face recognition with clustering based domain adaptation [57.29464116557734]
  We propose a new clustering-based domain adaptation method designed for face recognition task in which the source and target domain do not share any classes. Our method effectively learns the discriminative target feature by aligning the feature domain globally, and, at the meantime, distinguishing the target clusters locally.
  arXiv  Detail & Related papers  (2022-05-27T12:29:11Z)
• 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)
• From Big to Small: Adaptive Learning to Partial-Set Domains [94.92635970450578]
  Domain adaptation targets at knowledge acquisition and dissemination from a labeled source domain to an unlabeled target domain under distribution shift. Recent advances show that deep pre-trained models of large scale endow rich knowledge to tackle diverse downstream tasks of small scale. This paper introduces Partial Domain Adaptation (PDA), a learning paradigm that relaxes the identical class space assumption to that the source class space subsumes the target class space.
  arXiv  Detail & Related papers  (2022-03-14T07:02:45Z)
• Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering [119.88565565454378]
  Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain. We propose a hybrid model of Structurally Regularized Deep Clustering, which integrates the regularized discriminative clustering of target data with a generative one. Our proposed H-SRDC outperforms all the existing methods under both the inductive and transductive settings.
  arXiv  Detail & Related papers  (2020-12-08T08:52:00Z)
• Dual Distribution Alignment Network for Generalizable Person Re-Identification [174.36157174951603]
  Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID) We present a Dual Distribution Alignment Network (DDAN) which handles this challenge by selectively aligning distributions of multiple source domains. We evaluate our DDAN on a large-scale Domain Generalization Re-ID (DG Re-ID) benchmark.
  arXiv  Detail & Related papers  (2020-07-27T00:08:07Z)

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.