Memorizing Complementation Network for Few-Shot Class-Incremental Learning

@article{Ji2022MemorizingCN,
  title={Memorizing Complementation Network for Few-Shot Class-Incremental Learning},
  author={Zhong Ji and Zhi Hou and Xiyao Liu and Yanwei Pang and Xuelong Li},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.05610}
}
—Few-shot Class-Incremental Learning (FSCIL) aims at learning new concepts continually with only a few samples, which is prone to suffer the catastrophic forgetting and overfitting problems. The inaccessibility of old classes and the scarcity of the novel samples make it formidable to realize the trade-off between retaining old knowledge and learning novel concepts. Inspired by that different models memorize different knowledge when learn- ing novel concepts, we propose a Memorizing… 

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