Towards Label-Efficient Incremental Learning: A Survey

By Mert Kilickaya, Joost van de Weijer, Yuki M. Asano. 2023

5 citations recorded by the source corpus.

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.

Subfields: Meta & Continual Learning, Statistical Learning Theory

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Meta & Continual Learning

  1. Xingxing Zhang
  2. Liyuan Wang
  3. Hang Su
  4. Jun Zhu
  5. Simone Scardapane
  6. Valerio Marsocci
  7. Liyuan Wang
  8. Hongwei Yan

In Statistical Learning Theory

  1. Jun Zhu

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