A soft nearest-neighbor framework for continual semi-supervised learning

• Published in: arXiv.org
• Main Authors: Kang, Zhiqi, Fini, Enrico, Nabi, Moin, Ricci, Elisa, Alahari, Karteek
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 11.09.2023
• Subjects: Annotations; Representations; Semi-supervised learning
• ISSN: 2331-8422

Despite significant advances, the performance of state-of-the-art continual learning approaches hinges on the unrealistic scenario of fully labeled data. In this paper, we tackle this challenge and propose an approach for continual semi-supervised learning--a setting where not all the data samples are labeled. A primary issue in this scenario is the model forgetting representations of unlabeled data and overfitting the labeled samples. We leverage the power of nearest-neighbor classifiers to nonlinearly partition the feature space and flexibly model the underlying data distribution thanks to its non-parametric nature. This enables the model to learn a strong representation for the current task, and distill relevant information from previous tasks. We perform a thorough experimental evaluation and show that our method outperforms all the existing approaches by large margins, setting a solid state of the art on the continual semi-supervised learning paradigm. For example, on CIFAR-100 we surpass several others even when using at least 30 times less supervision (0.8% vs. 25% of annotations). Finally, our method works well on both low and high resolution images and scales seamlessly to more complex datasets such as ImageNet-100. The code is publicly available on https://github.com/kangzhiq/NNCSL