Adaptive Few-Shot Class-Incremental Learning via Latent Variable Models

Abstract

Approaches to class-incremental learning aim to successfully learn from continuously arriving classes. One added level of difficulty usually arises when the training data belonging to each class is scarce, which is the case in several open-world machine learning applications. In this paradigm, which is referred to as few-shot class-incremental learning, a typical learner needs to both be able to learn incrementally from the sequentially arriving classes, and preserve the knowledge which already exists about the old (i.e. already existing) classes. We propose a few-shot class-incremental learner which adapts the representations of the new few-shot classes as well as relevant previous knowledge based on a latent variable model. The proposed latent variable model is a form of a variational autoencoder that is designed to address the main challenges of the few-shot class-incremental learning paradigm, namely catastrophic forgetting and potential bias. During the few-shot learning of new classes, the amortization and high fidelity characteristics of the proposed model are leveraged to adapt not only the current class, but also the relevant previously encountered classes, in order to consistently mitigate the impact of catastrophic forgetting, bias and overfitting. We also derive a generalization upper bound on the error of an upcoming class. Experiments on several widely used few-shot class-incremental learning benchmarks, as well as a medical benchmark consisting of real-world medical images, demonstrate that the proposed model leads to improved performance, as measured by average overall and final classification accuracy, and in terms of alleviating catastrophic forgetting.