Lifelong Domain Adaptation via Consolidated Internal Distribution
We develop an algorithm to address unsupervised domain adaptation (UDA) in continual learning (CL) settings. The goal is to update a model continually to learn distributional shifts across sequentially arriving tasks with unlabeled data while retaining the knowledge about the past learned tasks. Existing UDA algorithms address the challenge of domain shift, but they require simultaneous access to the datasets of the source and the target domains. On the other hand, existing works on CL can handle tasks with labeled data. Our solution is based on consolidating the learned internal distribution for improved model generalization on new domains and benefitting from experience replay to overcome catastrophic forgetting.
Code (0)
등록된 구현이 없습니다.
Tasks
Continual LearningDomain AdaptationUnsupervised Domain AdaptationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Overcoming Concept Shift in Domain-Aware Settings through Consolidated Internal Distributions
We develop an algorithm to improve the performance of a pre-trained model under concept shift without retraining the model from scratch when only unannotated samples of initial concepts are accessible. We model this prob…
Domain AdaptationTransfer LearningUnsupervised Domain AdaptationA Probabilistic Framework for Lifelong Test-Time Adaptation
Test-time adaptation (TTA) is the problem of updating a pre-trained source model at inference time given test input(s) from a different target domain. Most existing TTA approaches assume the setting in which the target d…
Test-time AdaptationLifelong Self-Adaptation: Self-Adaptation Meets Lifelong Machine Learning
In the past years, machine learning (ML) has become a popular approach to support self-adaptation. While ML techniques enable dealing with several problems in self-adaptation, such as scalable decision-making, they are a…
BIG-bench Machine LearningDecision MakingSelf Adaptive SystemUltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Large Language Models
Lifelong learning enables large language models (LLMs) to adapt to evolving information by continually updating their internal knowledge. An ideal system should support efficient, wide-ranging updates while preserving ex…
GPULifelong learningModel EditingLifelong Domain Adaptive 3D Human Pose Estimation
3D Human Pose Estimation (3D HPE) is vital in various applications, from person re-identification and action recognition to virtual reality. However, the reliance on annotated 3D data collected in controlled environments…
Person Re-Identification3D Human Pose EstimationAction RecognitionDomain Adaptation