Continual Learning Without Knowing Task Identities: Rethinking Occam's Razor
Due to the catastrophic forgetting phenomenon of deep neural networks (DNNs), models trained in standard ways tend to forget what it has learned from previous tasks, especially when the new task is sufficiently different from the previous ones. To overcome this issue, various continual learning techniques have been developed in recent years, which, however, often suffer from a substantially increased model complexity and training time. In this paper, we illustrate that Occam's razor which suggests "entities should not be multiplied without necessity" carries to the development of efficient continual learning techniques. By proposing a relatively simple method based on Bayesian neural networks and model selection, we are able to significantly outperform various state-of-the-art techniques in terms of accuracy, model size, and running time. Moreover, unlike many existing works, our technique supports continual learning without task identity knowledge in training and inference phases, which is also known as task-free continual learning.
Code (0)
등록된 구현이 없습니다.
Tasks
Continual LearningModel SelectionSimilar Papers 제목 키워드 기반
Learning an evolved mixture model for task-free continual learning
Recently, continual learning (CL) has gained significant interest because it enables deep learning models to acquire new knowledge without forgetting previously learnt information. However, most existing works require kn…
Continual LearningDiversityRehearsal-free and Task-free Online Continual Learning With Contrastive Prompt
The main challenge of continual learning is \textit{catastrophic forgetting}. Because of processing data in one pass, online continual learning (OCL) is one of the most difficult continual learning scenarios. To address …
Continual LearningContinual Representation Learning for Biometric Identification
With the explosion of digital data in recent years, continuously learning new tasks from a stream of data without forgetting previously acquired knowledge has become increasingly important. In this paper, we propose a ne…
Continual LearningKnowledge DistillationRepresentation LearningKnowing when we do not know: Bayesian continual learning for sensing-based analysis tasks
Despite much research targeted at enabling conventional machine learning models to continually learn tasks and data distributions sequentially without forgetting the knowledge acquired, little effort has been devoted to …
Bayesian InferenceContinual LearningRethinking the Foundations for Continual Reinforcement Learning
Algorithms and approaches for continual reinforcement learning have gained increasing attention. Much of this early progress rests on the foundations and standard practices of traditional reinforcement learning, without …
Continual Learningreinforcement-learningReinforcement Learning