The official PyTorch implementation of Curriculum by Smoothing (NeurIPS 2020, Spotlight).
For any questions regarding the codebase, please send a message at: samarth.sinha@mail.utoronto.ca
USAGE: Simply use the code by running:
python3 main.py --dataset <DATASET> --alg <MODEL> --data <PATH_TO_DATA>
For example, to train a ResNet on CIFAR10 and the data is saved in ./data/, we can run:
python3 main.py --dataset cifar10 --alg res --data ./data/
For new expeiments it is important to tune the following hyperparameters:
--std --std_factor --epoch
LINK: https://arxiv.org/abs/2003.01367
This codebase has experiments for image classification and transfer learning.
If you use this codebase or find this repository helpful then please cite our paper:
@article{sinha2020curriculum,
title={Curriculum By Smoothing},
author={Sinha, Samarth and Garg, Animesh and Larochelle, Hugo},
journal={Advances in Neural Information Processing Systems},
volume={33},
year={2020}
}