WebJun 3, 2024 · Continual learning (CL) is less difficult for this class of models thanks to a simple key feature: instead of recalling the input-output relations of all previously seen data, task-conditioned hypernetworks only require rehearsing task-specific weight realizations, which can be maintained in memory using a simple regularizer. WebAbstract By learning a sequence of tasks continually, an agent in continual learning (CL) can improve the learning performance of both a new task and `old' tasks by leveraging the forward knowledge transfer and the backward knowledge transfer, respectively.

Vision-Intelligence-and-Robots-Group/Best-Incremental-Learning

Webconclude our paper in Section 5 and provide some directions for future research. 2 Preliminaries of Reinforcement learning Reinforcement learning [13] deals with … WebIn this paper, we address the problem of catastrophic forgetting in a fixed capacity network when data from the old tasks are not available. To mitigate forgetting, our approach puts explicit constraints ... Continual Learning: We consider supervised learning setup where T tasks are learned sequen-tially. Each task has a task descriptor ... ratp greves jeudi

Continual Learning of Hand Gestures for Human-Robot Interaction

WebApr 1, 2024 · Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed … WebJun 15, 2016 · The progressive networks approach represents a step forward in this direction: they are immune to forgetting and can leverage prior knowledge via lateral … dr. sina matin grapevine