Complementary Asymmetric Representation Learning for Exemplar-Free Class-Incremental Learning

Abstract

Exemplar-free class-incremental learning (EFCIL) poses the challenge that models cannot access data from previous tasks when learning new classes, leading to catastrophic forgetting. Recent methods freeze the feature extractor after the initial task and adapt only the classification mechanism to new classes, achieving strong performance. However, they largely overlook improving the frozen feature extractor's adaptability to unseen classes, which limits further performance gains. To address this limitation, we propose a novel complementary asymmetric representation learning (CARL) framework to enhance the model's adaptability to unseen classes. The core of CARL is the asymmetric representation learning (ARL) architecture, which combines a base encoder that extracts discriminative features for the initial task with a projection multilayer perceptron (MLP) head appended to its output. By introducing the projection head, the original output representation of the base encoder becomes an intermediate representation in the projected branch, encouraging the encoder to learn more generalizable features. This allows the model to preserve discriminative representations for the initially learned classes while improving its adaptability to unseen classes. In addition, the CARL framework employs two parallel ARL branches and aggregates their Mahalanobis distances during inference to improve prediction performance. To evaluate the efficacy of CARL, we compare it with recent methods using three widely recognized datasets. The proposed approach improves average accuracy over the best competing method by 3.70 percentage points on CIFAR-100, 2.63 percentage points on Tiny-ImageNet, and 2.90 percentage points on ImageNet-Subset.