Task Agnostic Representation Consolidation: a Self-supervised based Continual Learning Approach

By Prashant Bhat, Bahram Zonooz, Elahe Arani. 2022

16 citations recorded by the source corpus.

Continual learning (CL) over non-stationary data streams remains one of the long-standing challenges in deep neural networks (DNNs) as they are prone to catastrophic forgetting. CL models can benefit from self-supervised pre-training as it enables learning more generalizable task-agnostic features. However, the effect of self-supervised pre-training diminishes as the length of task sequences increases. Furthermore, the domain shift between pre-training data distribution and the task distribution reduces the generalizability of the learned representations. To address these limitations, we propose Task Agnostic Representation Consolidation (TARC), a two-stage training paradigm for CL that intertwines task-agnostic and task-specific learning whereby self-supervised training is followed by supervised learning for each task. To further restrict the deviation from the learned representations in the self-supervised stage, we employ a task-agnostic auxiliary loss during the supervised stage. We show that our training paradigm can be easily added to memory- or regularization-based approaches and provides consistent performance gain across more challenging CL settings. We further show that it leads to more robust and well-calibrated models.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Continual Learning, Fast and Slow (2021, background): “Itisworthnotingthat usingonlytheslowlearnertolearnbothrepresentationsisthe strategyinconcurrentworks[55],[56].”