[Paper] [Presentation] [Poster]
If you use this code in your research, please cite
@inproceedings{aaai19-jiangchao,
title = {Safeguarded Dynamic Label Regression for Noisy Supervision},
author = {Jiangchao Yao, Hao Wu, Ya Zhang, Ivor W. Tsang and Jun Sun},
booktitle = {Proceedings of the Association for the Advancement of Artificial Intelligence Conference on
Artificial Intelligence, {AAAI-19}},
publisher = {Association for the Advancement of Artificial Intelligence Conference},
year = {2019}
}
and its extended version
@article{yao2019safeguarded,
title={Safeguarded Dynamic Label Regression for Generalized Noisy Supervision},
author={Yao, Jiangchao and Zhang, Ya and Tsang, Ivor W and Sun, Jun},
journal={arXiv preprint arXiv:1903.02152},
year={2019}
}
python dataset.py- Train DNNs directly with the cross-entropy loss (CE).
python cifar10_train.py --train_dir results/events_ce/cifar10_train --noise_rate 0.3 # You can train other models like this one- Train Bootstrapping
python cifar10_train_bootstrapping.py --train_dir results/events_bootstrapping/cifar10_train --noise_rate 0.3 # You can train other models like this one- Train Forward
python cifar10_train_T.py --init_dir results/events_ce/cifar10_train --train_dir results/events_T/cifar10_train --noise_rate 0.3 # You can train other models like this one- Train S-adaptation
python cifar10_train_varT.py --init_dir results/events_ce/cifar10_train --train_dir results/events_varT/cifar10_train --noise_rate 0.3 # You can train other models like this one- Train LCCN
python cifar10_train_varC.py --init_dir results/events_ce/cifar10_train --train_dir results/events_varC/cifar10_train --noise_rate 0.3 # You can train other models like this oneNote that step 3, 4, 5 are based on the classifier pretraining in step 1 and among them, they also have the internal inheritance relationship due to the transition matrix initialization. Please execute them sequentially. However, this does not mean a shortcoming of the model. You can easily modify them into parallel versions by integrating the classifier pretraining and transition initialization into one script.
# open a new shell. (Take CE as the example)
python cifar10_eval.py --checkpoint_dir results/events_ce/cifar10_train --eval_dir results/cifar10_eval # open a new shell. (Take CE as the example)
tensorboard --logdir=results/events_ce --port=8080This is the code of the Clothing-1M experiments. Similarly, we have provided multiple scripts to run the baselines and our model. The parameters also have been hard-coded in the script.
In addition, it is almost the same code of the WebVision experiments. You can simply organize the data structure of WebVision like Clothing1M and run the codes.