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IDPOC: Class Incremental Learning Based on Identically Distributed Parallel One-Class Classifiers

Paper

Official repository of Class Incremental Learning Based on Identically Distributed Parallel One-Class Classifiers

Published at NeuroComputing

geometry

Setup

  • Use ./utils/main.py to run experiments.
  • Some training result can be found in folder ./result.

Datasets

Class-IL / Task-IL settings

  • Sequential MNIST
  • Sequential CIFAR-10
  • Sequential CIFAR-100
  • Sequential Tiny ImageNet

Performance

MNIST CIFAR10 CIFAR100 Tiny-ImageNet
DER++ 85.61 64.88 24.75 10.96
LWF 21.62 19.59 9.20 9.36
OWM 96.30 52.83 27.63 15.30
ILCOC 86.05 38.40 24.39 16.97
DisCOIL 96.69 44.54 27.50 19.75
IDPOC 87.51 55.50 29.08 22.55

Visualizaiton of score distributions

score

Requirement

  • numpy==1.16.4
  • Pillow==6.1.0
  • torch==1.3.1
  • torchvision==0.4.2

Related repository

https://github.com/aimagelab/mammoth

Citation

@article{sun2023class,

title={Class Incremental Learning based on Identically Distributed Parallel One-Class Classifiers},

author={Sun, Wenju and Li, Qingyong and Zhang, Jing and Wang, Wen and Geng, YangLi-ao},

journal={Neurocomputing},

volume={556},

pages={126579},

year={2023},

publisher={Elsevier}

}