  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.

Machine Learning;Computer Vision and Pattern Recognition