Related papers: Towards Label-Efficient Incremental Learning: A Survey

Towards Label-Efficient Incremental Learning: A Survey

URL: http://arxiv.org/abs/2302.00353v2
Date: Thu, 2 Feb 2023 08:57:01 GMT
Title: Towards Label-Efficient Incremental Learning: A Survey
Authors: Mert Kilickaya, Joost van de Weijer and Yuki M. Asano
Abstract summary: We study incremental learning, where a learner is required to adapt to an incoming stream of data with a varying distribution. We identify three subdivisions, namely semi-, few-shot- and self-supervised learning to reduce labeling efforts.
Score: 42.603603392991715
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The current dominant paradigm when building a machine learning model is to iterate over a dataset over and over until convergence. Such an approach is non-incremental, as it assumes access to all images of all categories at once. However, for many applications, non-incremental learning is unrealistic. To that end, researchers study incremental learning, where a learner is required to adapt to an incoming stream of data with a varying distribution while preventing forgetting of past knowledge. Significant progress has been made, however, the vast majority of works focus on the fully supervised setting, making these algorithms label-hungry thus limiting their real-life deployment. To that end, in this paper, we make the first attempt to survey recently growing interest in label-efficient incremental learning. We identify three subdivisions, namely semi-, few-shot- and self-supervised learning to reduce labeling efforts. Finally, we identify novel directions that can further enhance label-efficiency and improve incremental learning scalability. Project website: https://github.com/kilickaya/label-efficient-il.

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