This work addresses the challenging, real-world problem of Noisy-Label Class Incremental Learning, where label noise adversely affects the performance at each incremental task. Towards this goal, we propose a two-stage approach leveraging pre-trained models effectively and information from neighbouring samples. In the first stage, we introduce a neighbour-guidance cross-entropy loss function for feature learning, where the contributions of each data sample is computed based on the labels of neighbouring samples in the feature space, which also evolves as training progresses. This adaptive loss prioritizes reliable samples and mitigates the influence of noisy ones. In the second stage, classifiers are refined using weighted class statistics, which is also guided by the neighbourhood information. This step further enhances the model’s performance by aligning classifiers with the learned class distributions. The proposed GeNGuide (Generalized Neighbour Guidance) framework works seamlessly without any modifications for several scenarios of class-incremental learning, namely (i) when the class-labels have varying amount of noise, including the noise-less case; (ii) when, in addition to the presence of noisy labels, the data is imbalanced, which makes the problem even more challenging. To the best of our knowledge, this is the first work which takes a step towards building generalized models which achieves state-of-the-art performance on several challenging class-incremental learning protocols, thereby justifying its effectiveness. https://github.com/avnCode/GeNGuide.git

We propose a novel TACLE (TAsk and CLass-awarE) framework for the relatively unexplored and challenging problem of exemplar-free semi-supervised class incremen-tal learning. In this scenario, at each new task, the model has to learn new classes from both (few) labeled and unlabeled data without access to exemplars from previous classes. In addition to leveraging the capabilities of pretrained models, TACLE proposes a novel task-adaptive threshold, thereby maximizing the utilization of the available unlabeled data as incremental learning progresses. Additionally, to enhance the performance of the under-represented classes within each task, we propose a class-aware weighted cross-entropy loss. We also exploit the unlabeled data for classifier alignment, which further enhances the model performance. Extensive experiments on benchmark datasets, namely CIFAR10, CIFAR100, and ImageNet-Subset100 demonstrate the effectiveness of the proposed TACLE framework. We further showcase its effectiveness when the unlabeled data is imbalanced and also for the extreme case of one labeled example per class. code: https://github.com/rokmr/TACLE

This paper introduces a two-stage framework designed to enhance long-tail class incremental learning, enabling the model to progressively learn new classes, while mitigating catastrophic forgetting in the context of long-tailed data distributions. Addressing the challenge posed by the under-representation of tail classes in long-tail class incremental learning, our approach achieves classifier alignment by leveraging global variance as an informative measure and class prototypes in the second stage. This process effectively captures class properties and eliminates the need for data balancing or additional layer tuning. Alongside traditional class incremental learning losses in the first stage, the proposed approach incorporates mixup classes to learn robust feature representations, ensuring smoother boundaries. The proposed framework can seamlessly integrate as a module with any class incremental learning method to effectively handle long-tail class incremental learning scenarios. Extensive experimentation on the CIFAR-100 and ImageNet-Subset datasets validates the approach’s efficacy, showcasing its superiority over state-of-the-art techniques across various long-tail CIL settings. Code is available at https://github.com/JAYATEJAK/GVAlign.