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Continual learning by asymmetric loss approximation with single-side overestimation

This repository ( https://github.com/dmpark04/alasso ) contains code to reproduce the key findings of:

Park, D., Hong, S., Han, B., & Lee, K. M. (2019). Continual learning by asymmetric loss approximation with single-side overestimation. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3335-3344).

http://openaccess.thecvf.com/content_ICCV_2019/html/Park_Continual_Learning_by_Asymmetric_Loss_Approximation_With_Single-Side_Overestimation_ICCV_2019_paper.html

BibTeX

@inproceedings{park2019continual,
  title={Continual learning by asymmetric loss approximation with single-side overestimation},
  author={Park, Dongmin and Hong, Seokil and Han, Bohyung and Lee, Kyoung Mu},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={3335--3344},
  year={2019}
}

Requirements

We have tested this code with the following configuration:

  • Python 3.6.9
  • Tensorflow 1.13.1
  • Keras 2.0.5

How to get source codes

git clone https://github.com/dmpark04/alasso.git

How to run

For 30 tasks, run the following commands.

cd permuted_minst
python train.py
python 'Basic graph Permuted MNIST.py'

For 100 tasks, run the following commands.

cd permuted_minst
python train_100.py
python 'Basic graph Permuted MNIST.py'

About

Code to accompany our paper "Continual learning by asymmetric loss approximation with single-side overestimation" ICCV 2019

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