  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.
