S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning

Kalla, J and Biswas, S (2022) S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning. In: 17th European Conference on Computer Vision, ECCV 2022, 23 - 27 October 2022, Tel Aviv, pp. 432-448.

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Abstract

Few-shot class-incremental learning (FSCIL) aims to learn progressively about new classes with very few labeled samples, without forgetting the knowledge of already learnt classes. FSCIL suffers from two major challenges: (i) over-fitting on the new classes due to limited amount of data, (ii) catastrophically forgetting about the old classes due to unavailability of data from these classes in the incremental stages. In this work, we propose a self-supervised stochastic classifier (S3C) (code: https://github.com/JAYATEJAK/S3C ) to counter both these challenges in FSCIL. The stochasticity of the classifier weights (or class prototypes) not only mitigates the adverse effect of absence of large number of samples of the new classes, but also the absence of samples from previously learnt classes during the incremental steps. This is complemented by the self-supervision component, which helps to learn features from the base classes which generalize well to unseen classes that are encountered in future, thus reducing catastrophic forgetting. Extensive evaluation on three benchmark datasets using multiple evaluation metrics show the effectiveness of the proposed framework. We also experiment on two additional realistic scenarios of FSCIL, namely where the number of annotated data available for each of the new classes can be different, and also where the number of base classes is much lesser, and show that the proposed S3C performs significantly better than the state-of-the-art for all these challenging scenarios. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Item Type:	Conference Paper
Publication:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher:	Springer Science and Business Media Deutschland GmbH
Additional Information:	The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH.
Keywords:	Machine learning, Adverse effect; Base class; Few-shot class-incremental learning; Incremental learning; Learn+; Overfitting; Self-supervised learning; Stochastic classifier; Stochasticity; Stochastics, Stochastic systems
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	09 Jan 2023 09:03
Last Modified:	09 Jan 2023 09:03
URI:	https://eprints.iisc.ac.in/id/eprint/78933

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