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Learning an evolved mixture model for task-free continual learning
[article]
2022
arXiv
pre-print
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 knowing the task identities and boundaries, which is not realistic in a real context. In this paper, we address a more challenging and realistic setting in CL, namely the Task-Free Continual Learning (TFCL) in which a model is trained on non-stationary data streams with no
arXiv:2207.05080v1
fatcat:l77bjai5gzfwbjktwpmsljtpqa