IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual Learning
This addresses the problem of catastrophic forgetting in deep neural networks for continual learning, offering an incremental improvement over existing rehearsal-based methods.
The paper tackles catastrophic forgetting in continual learning by proposing IMEX-Reg, a method combining implicit-explicit regularization with contrastive representation learning and consistency regularization, which significantly improves generalization performance and outperforms rehearsal-based approaches in low-buffer regimes.
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 regularization approach using contrastive representation learning (CRL) and consistency regularization. To further leverage the global relationship between representations learned using CRL, we propose a regularization strategy to guide the classifier 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.