Learning an Evolved Mixture Model for Task-Free Continual Learning
By Fei Ye, Adrian G. Bors. 2022
10 citations recorded by the source corpus.
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 explicit task information. To address TFCL, we introduce an evolved mixture model whose network architecture is dynamically expanded to adapt to the data distribution shift. We implement this expansion mechanism by evaluating the probability distance between the knowledge stored in each mixture model component and the current memory buffer using the Hilbert Schmidt Independence Criterion (HSIC). We further introduce two simple dropout mechanisms to selectively remove stored examples in order to avoid memory overload while preserving memory diversity. Empirical results demonstrate that the proposed approach achieves excellent performance.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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Citations in the corpus
- Continual Variational Autoencoder Learning via Online Cooperative Memorization (2022, background): “This issue can be solved by storing the information of past samples into the network’s parameters which are then frozen when learning novel tasks [35,64,65,67,68].”