Skip to content
Code to accompany our paper "Continual learning by asymmetric loss approximation with single-side overestimation" ICCV 2019
Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
alasso
docs
permuted_mnist
LICENSE
README.md

README.md

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'
You can’t perform that action at this time.