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 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.
Abstract (translated)
持续学习（CL）是深度神经网络中一个长期存在的挑战，因为之前学习的知识会因为梯度消失而丢失。尽管基于练习的方法在减轻梯度消失方面相当成功，但它们在缓冲样本和先验信息损失方面过于拟合，阻碍了在低缓冲 regime下的泛化能力。受到人类使用强归纳偏见学习的方式启发，我们提出了一种基于对比学习（CRL）的低缓冲时经验回放（IMEX-Reg）方法，以提高在低缓冲 regime 下 CL 的泛化性能。具体来说，我们采用对比表示学习（CRL）中的双峰隐式-显式正则化方法，并结合一致性正则化。为了更好地利用使用 CRL 学习到的表示之间的关系，我们提出了一种引导分类器朝 CRL 中单位球体激活关联的方向的规范化策略。我们的结果表明，IMEX-Reg 显著提高了泛化性能，在多个 CL 场景中超过了基于练习的方法。它还对于自然和对抗性污染具有较低的任务晚期偏见。此外，我们还提供了进一步支持我们设计决策的理论和实验洞察。
URL
https://arxiv.org/abs/2404.18161
