Learning an evolved mixture model for task-free continual learning
- URL: http://arxiv.org/abs/2207.05080v1
- Date: Mon, 11 Jul 2022 16:01:27 GMT
- Title: Learning an evolved mixture model for task-free continual learning
- Authors: Fei Ye and Adrian G. Bors
- Abstract summary: We address the Task-Free Continual Learning (TFCL) in which a model is trained on non-stationary data streams with no explicit task information.
We introduce two simple dropout mechanisms to selectively remove stored examples in order to avoid memory overload.
- Score: 11.540150938141034
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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