Domain Generalizable Continual Learning

• URL: http://arxiv.org/abs/2510.16914v1
• Date: Sun, 19 Oct 2025 16:16:20 GMT
• Title: Domain Generalizable Continual Learning
• Authors: Hongwei Yan, Guanglong Sun, Zhiqi Kang, Yi Zhong, Liyuan Wang,
• Abstract summary: We introduce a novel and realistic setting named domain generalizable continual learning (DGCL)<n>A model learns sequential tasks with each involving a single domain, aiming to perform well across all encountered tasks and domains.<n>This setting poses unique challenges in acquiring, retaining, and leveraging both semantic- and domain-relevant information for robust generalization.<n>We propose adaptive Domain Transformation (DoT), an innovative PTMs-based approach tailored to DGCL.
• Score: 24.99964797750464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To adapt effectively to dynamic real-world environments, intelligent systems must continually acquire new skills while generalizing them to diverse, unseen scenarios. Here, we introduce a novel and realistic setting named domain generalizable continual learning (DGCL): a model learns sequential tasks with each involving a single domain, aiming to perform well across all encountered tasks and domains. This setting poses unique challenges in acquiring, retaining, and leveraging both semantic- and domain-relevant information for robust generalization. Although state-of-the-art continual learning (CL) methods have employed pre-trained models (PTMs) to enhance task-specific generalization, they typically assume identical training and testing domains for each task and therefore perform poorly in DGCL. To this end, we propose adaptive Domain Transformation (DoT), an innovative PTMs-based approach tailored to DGCL. Inspired by the distributed-plus-hub theory of the human brain, DoT disentangles semantic- and domain-relevant information in representation learning, and adaptively transforms task representations across various domains for output alignment, ensuring balanced and generalized predictions. DoT serves as a plug-in strategy that greatly facilitates state-of-the-art CL baselines under both full parameter tuning and parameter-efficient tuning paradigms in DGCL, validated by extensive experiments. Also, DoT is shown to accumulate domain-generalizable knowledge from DGCL, and ensure resource efficiency with a lightweight implementation.

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