Machine learning models often suffer from catastrophic forgetting of previously learned knowledge when learning new classes. Various methods have been proposed to mitigate this issue. However, rehearsal-based learning, which retains samples from previous classes, typically achieves good performance but tends to memorize specific instances, struggling with Out-of-Distribution (OOD) generalization. This often leads to high forgetting rates and poor generalization. Surprisingly, the OOD generalization capabilities of these methods have been largely unexplored. In this paper, we highlight this issue and propose a simple yet effective strategy inspired by contrastive learning and data-centric principles to address it. We introduce Adaptive Contrastive Replay (ACR), a method that employs dual optimization to simultaneously train both the encoder and the classifier. ACR adaptively populates the replay buffer with misclassified samples while ensuring a balanced representation of classes and tasks. By refining the decision boundary in this way, ACR achieves a balance between stability and plasticity. Our method significantly outperforms previous approaches in terms of OOD generalization, achieving an improvement of 13.41\% on Split CIFAR-100, 9.91\% on Split Mini-ImageNet, and 5.98\% on Split Tiny-ImageNet.

该研究针对机器学习模型在新类别学习中容易造成的灾难性遗忘问题，提出了一种新颖的自适应对比重放（ACR）策略。该方法通过双重优化策略改进重放缓冲区，从而平衡稳定性与可塑性，显著提高了OOD泛化能力，达到了在多个数据集上超过以往方法的显著提升效果。

持续学习：通过自适应对比重放减少遗忘，提高OOD泛化能力