Balanced softmax cross-entropy for incremental learning with and without memory

• Published in: Computer vision and image understanding Vol. 225; p. 103582
• Main Authors: Jodelet, Quentin, Liu, Xin, Murata, Tsuyoshi
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.12.2022
• Subjects: Bias mitigation; Class incremental learning; Continual learning; Image classification; Bias mitigation; Continual learning; Class incremental learning; Image classification
• ISSN: 1077-3142; 1090-235X
• DOI: 10.1016/j.cviu.2022.103582

When incrementally trained on new classes, deep neural networks are subject to catastrophic forgetting which leads to an extreme deterioration of their performance on the old classes while learning the new ones. Using a small memory containing few samples from past classes has shown to be an effective method to mitigate catastrophic forgetting. However, due to the limited size of the replay memory, there is a large imbalance between the number of samples for the new and the old classes in the training dataset resulting in bias in the final model. To address this issue, we propose to use the Balanced Softmax Cross-Entropy and show that it can be seamlessly combined with state-of-the-art approaches for class-incremental learning in order to improve their accuracy while also potentially decreasing the computational cost of the training procedure. We further extend this approach to the more demanding class-incremental learning without memory setting and achieve competitive results with memory-based approaches. Experiments on the challenging ImageNet, ImageNet-Subset, and CIFAR100 benchmarks with various settings demonstrate the benefits of our approach. •Proposed to use Balanced Softmax for bias mitigation in class-incremental learning.•Can be used for class-incremental learning with and without memory.•Seamlessly combinable with state-of-the-art approaches to improve accuracy.•Can reduce training complexity by removing the need for a balanced finetuning step.