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Mnemonics Training: Multi-Class Incremental Learning without Forgetting

Yaoyao Liu1   Yuting Su2   An-An Liu2   Bernt Schiele1   Qianru Sun3

1Max Planck Institute for Informatics   2Tianjin University   3Singapore Management University  
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (Oral)



Multi-Class Incremental Learning (MCIL) aims to learn new concepts by incrementally updating a model trained on previous concepts. However, there is an inherent trade-off to effectively learning new concepts without catastrophic forgetting of previous ones. To alleviate this issue, it has been proposed to keep around a few examples of the previous concepts but the effectiveness of this approach heavily depends on the representativeness of these examples. This paper proposes a novel and automatic framework we call mnemonics, where we parameterize exemplars and make them optimizable in an end-to-end manner. We train the framework through bilevel optimizations, i.e., model-level and exemplar-level. We conduct extensive experiments on three MCIL benchmarks, CIFAR-100, ImageNet-Subset and ImageNet, and show that using mnemonics exemplars can surpass the state-of-the-art by a large margin. Interestingly and quite intriguingly, the mnemonics exemplars tend to be on the boundaries between classes.


Oral Talk (subtitles available)


The computing flow of the proposed mnemonics training. It is a global BOP that alternates the learning of mnemonics exemplars (we call exemplar-level optimization) and MCIL models (model-level optimization).


t-SNE Visualization


Please cite our paper if it is helpful to your work:

author    = {Liu, Yaoyao and Su, Yuting and Liu, An{-}An and Schiele, Bernt and Sun, Qianru},
title     = {Mnemonics Training: Multi-Class Incremental Learning without Forgetting},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
pages     = {12245--12254},
year      = {2020}


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