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Topology-Preserving Class-Incremental Learning

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12364)

Abstract

A well-known issue for class-incremental learning is the catastrophic forgetting phenomenon, where the network’s recognition performance on old classes degrades severely when incrementally learning new classes. To alleviate forgetting, we put forward to preserve the old class knowledge by maintaining the topology of the network’s feature space. On this basis, we propose a novel topology-preserving class-incremental learning (TPCIL) framework. TPCIL uses an elastic Hebbian graph (EHG) to model the feature space topology, which is constructed with the competitive Hebbian learning rule. To maintain the topology, we develop the topology-preserving loss (TPL) that penalizes the changes of EHG’s neighboring relationships during incremental learning phases. Comprehensive experiments on CIFAR100, ImageNet, and subImageNet datasets demonstrate the power of the TPCIL for continuously learning new classes with less forgetting. The code will be released.

Keywords

Topology-Preserving Class-Incremental Learning (TPCIL) Class-Incremental Learning (CIL) Elastic Hebbian Graph (EHG) Topology-Preserving Loss (TPL) 

Notes

Acknowledgements

This work is sponsored by National Key R&D Program of China under Grand No.2019YFB1312000, National Major Project under Grant No.2017YFC0803905 and SHAANXI Province Joint Key Laboratory of Machine Learning.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.School of Software EngineeringXi’an Jiaotong UniversityXi’anChina
  3. 3.Research Center for Artificial Intelligence, Peng Cheng LaboratoryShenzhenChina

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