OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning
This addresses the challenge of incremental learning with scarce data for machine learning systems, though it appears incremental as it builds on existing contrastive and orthogonality techniques.
The paper tackles the problem of few-shot class-incremental learning, where models must learn new classes with limited data while avoiding catastrophic forgetting, and achieves state-of-the-art performance on benchmark datasets like mini-ImageNet, CIFAR100, and CUB.
Few-Shot Class-Incremental Learning (FSCIL) introduces a paradigm in which the problem space expands with limited data. FSCIL methods inherently face the challenge of catastrophic forgetting as data arrives incrementally, making models susceptible to overwriting previously acquired knowledge. Moreover, given the scarcity of labeled samples available at any given time, models may be prone to overfitting and find it challenging to strike a balance between extensive pretraining and the limited incremental data. To address these challenges, we propose the OrCo framework built on two core principles: features' orthogonality in the representation space, and contrastive learning. In particular, we improve the generalization of the embedding space by employing a combination of supervised and self-supervised contrastive losses during the pretraining phase. Additionally, we introduce OrCo loss to address challenges arising from data limitations during incremental sessions. Through feature space perturbations and orthogonality between classes, the OrCo loss maximizes margins and reserves space for the following incremental data. This, in turn, ensures the accommodation of incoming classes in the feature space without compromising previously acquired knowledge. Our experimental results showcase state-of-the-art performance across three benchmark datasets, including mini-ImageNet, CIFAR100, and CUB datasets. Code is available at https://github.com/noorahmedds/OrCo