Continual learning, also known as lifelong learning or incremental learning, refers to the process by which a model learns from a stream of incoming data over time. A common problem in continual learning is the classification layer's bias towards the most recent task. Traditionally, methods have relied on incorporating data from past tasks during training to mitigate this issue. However, the recent shift in continual learning to memory-free environments has rendered these approaches infeasible. In this study, we propose a solution focused on the testing phase. We first introduce a simple Out-of-Task Detection method, OTD, designed to accurately identify samples from past tasks during testing. Leveraging OTD, we then propose: (1) an Adaptive Retention mechanism for dynamically tuning the classifier layer on past task data; (2) an Adaptive Correction mechanism for revising predictions when the model classifies data from previous tasks into classes from the current task. We name our approach Adaptive Retention & Correction (ARC). While designed for memory-free environments, ARC also proves effective in memory-based settings. Extensive experiments show that our proposed method can be plugged in to virtually any existing continual learning approach without requiring any modifications to its training procedure. Specifically, when integrated with state-of-the-art approaches, ARC achieves an average performance increase of 2.7% and 2.6% on the CIFAR-100 and Imagenet-R datasets, respectively.

我们提出了一种基于在测试阶段的解决方案，引入了一个简单的任务外检测方法（OTD），用于准确识别来自过去任务的样本，借助OTD，我们提出了自适应保留机制和自适应修正机制，称之为Adaptive Retention & Correction（ARC），实验证明我们的方法可以与现有的持续学习方法相结合，而无需修改其训练程序，在CIFAR-100和Imagenet-R数据集上分别实现了2.7%和2.6%的平均性能提升。

持续学习的自适应保留与修正