IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual Learning

Prashant Shivaram Bhat, Bharath Chennamkulam Renjith, Bahram Zonooz, Elahe Arani

Primary Area: transfer learning, meta learning, and lifelong learning

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Keywords: Continual Learning, Lifelong Learning, Inductive Bias, Multitask Learning, Catastrophic Forgetting, Experience Rehearsal, Task Attention

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Abstract:

Continual learning (CL) remains one of the long-standing challenges for deep
neural networks due to catastrophic forgetting of previously acquired knowledge.
Although rehearsal-based approaches have been fairly successful in mitigating
catastrophic forgetting, they suffer from overfitting on buffered samples and prior
information loss, hindering generalization under low-buffer regimes. Inspired
by how humans learn using strong inductive biases, we propose IMEX-Reg to
improve the generalization performance of experience rehearsal in CL under low
buffer regimes. Specifically, we employ a two-pronged implicit-explicit regular-
ization approach using contrastive representation learning (CRL) and consistency
regularization. To further leverage the global relationship between representations
learned using CRL, we propose a novel regularization strategy to guide the clas-
sifier toward the activation correlations in the unit hypersphere of the CRL. Our
results show that IMEX-Reg significantly improves generalization performance and
outperforms rehearsal-based approaches in several CL scenarios. It is also robust
to natural and adversarial corruptions with less task-recency bias. Additionally, we
provide theoretical insights to support our design decisions further.

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Submission Number: 7749