Rethinking few-shot class-incremental learning: A lazy learning baseline

Author:

Qin ZhiliORCID,Han WeiORCID,Liu JiamingORCID,Zhang RuiORCID,Yang Qingli,Sun ZejunORCID,Shao JunmingORCID

Publisher

Elsevier BV

Reference30 articles.

1. Agarwal, A., Banerjee, B., Cuzzolin, F., & Chaudhuri, S. (2022). Semantics-Driven Generative Replay for Few-Shot Class Incremental Learning. In Proceedings of the 30th ACM international conference on multimedia (pp. 5246–5254).

2. Akyürek, A. F., Akyürek, E., Wijaya, D., & Andreas, J. (2021). Subspace Regularizers for Few-Shot Class Incremental Learning. In International conference on learning representations.

3. Few-shot incremental learning in the context of solar cell quality inspection;Balzategui;Expert Systems with Applications,2023

4. Castro, F. M., Marín-Jiménez, M. J., Guil, N., Schmid, C., & Alahari, K. (2018). End-to-end incremental learning. In Proceedings of the European conference on computer vision (pp. 233–248).

5. Chen, K., & Lee, C.-G. (2020). Incremental few-shot learning via vector quantization in deep embedded space. In International conference on learning representations.

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