Buffer-free Class-Incremental Learning with Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2505.23412v1
• Date: Thu, 29 May 2025 13:01:00 GMT
• Title: Buffer-free Class-Incremental Learning with Out-of-Distribution Detection
• Authors: Srishti Gupta, Daniele Angioni, Maura Pintor, Ambra Demontis, Lea Schönherr, Battista Biggio, Fabio Roli,
• Abstract summary: Class-incremental learning (CIL) poses significant challenges in open-world scenarios.<n>We present an in-depth analysis of post-hoc OOD detection methods and investigate their potential to eliminate the need for a memory buffer.<n>We show that this buffer-free approach achieves comparable or superior performance to buffer-based methods both in terms of class-incremental learning and the rejection of unknown samples.
• Score: 18.706435793435094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Class-incremental learning (CIL) poses significant challenges in open-world scenarios, where models must not only learn new classes over time without forgetting previous ones but also handle inputs from unknown classes that a closed-set model would misclassify. Recent works address both issues by (i)~training multi-head models using the task-incremental learning framework, and (ii) predicting the task identity employing out-of-distribution (OOD) detectors. While effective, the latter mainly relies on joint training with a memory buffer of past data, raising concerns around privacy, scalability, and increased training time. In this paper, we present an in-depth analysis of post-hoc OOD detection methods and investigate their potential to eliminate the need for a memory buffer. We uncover that these methods, when applied appropriately at inference time, can serve as a strong substitute for buffer-based OOD detection. We show that this buffer-free approach achieves comparable or superior performance to buffer-based methods both in terms of class-incremental learning and the rejection of unknown samples. Experimental results on CIFAR-10, CIFAR-100 and Tiny ImageNet datasets support our findings, offering new insights into the design of efficient and privacy-preserving CIL systems for open-world settings.

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