Kalla, J and Biswas, S (2024) Robust Feature Learning and Global Variance-Driven Classifier Alignment for Long-Tail Class Incremental Learning. In: UNSPECIFIED, pp. 32-41.
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Abstract
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. © 2024 IEEE.
| Item Type: | Conference Paper |
|---|---|
| Publication: | Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | The copyright for this article belongs to authors. |
| Department/Centre: | Division of Electrical Sciences > Electrical Engineering |
| Date Deposited: | 24 May 2024 05:28 |
| Last Modified: | 24 May 2024 05:28 |
| URI: | https://eprints.iisc.ac.in/id/eprint/85044 |
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