On Class-Incremental Learning for Fully Binarized Convolutional Neural Networks

Recent advances in Binary Neural Networks (BNNs) are opening up new possibilities for disruptive hardware accelerators. This paper extends prior work on incremental learning to BNNs, by proposing a specifically-designed fully-binarized network and evaluating it on two learning variants, i.e., native and latent replay. The proposed BNN achieves a 53.3% test accuracy on the CIFAR-100 benchmark while relying on a binary-only arithmetic, for a 4.1Mb model size. Given a class-incremental learning experimental setup, we evaluate the influence of replay buffer size on the strategy, highlighting a turning point where latent replay offers a better classification performance than Native replay. In addition, our approach exhibits robustness against a large number of successive retrainings with an accuracy always 10% higher than a full-precision counterpart.