Enhancing Feature Learning With Hard Samples in Mutual Learning for Online Class Incremental Learning

Online Class-Incremental Learning (OCIL) aims to solve the problem of incrementally learning new classes from a non-i.i.d. and single-pass data stream. Compared to the offline setting, OCIL is much closer to a live learning experience requiring higher model update frequency at less computational budget. Due to its one-epoch training constraint, the model is likely to learn non-essential features and encounter the under-fitting issue, which severely affects the model’s stability. In this paper, we investigate how to use hard samples to improve data variability, eventually enhancing feature learning and addressing the under-fitting problem. Specifically, by introducing a scoring function assessing the sample value, we build an OCIL formulation that simultaneously generates high-value samples and optimizes the OCIL model, improving generalization ability within the constraint of single-epoch training. Empirically, we found that strong data augmentation is a simple but effective way to generate a higher proportion of high-score samples. To make the most of these augmented samples, we design an OCIL model based on mutual learning with two networks of identical structures. Moreover, a collaborative learning mechanism is developed by aligning the features and class probabilities from the two networks to promote their interaction. Extensive experiments on three widely used datasets for OCIL have demonstrated the effectiveness of our method, obtaining superior performance to state-of-the-art methods.