Code to reproduce some of the main results in:
Sylvestre-Alvise Rebuffi, Sebastien Ehrhardt, Kai Han, Andrea Vedaldi, Andrew Zisserman, "Semi-Supervised Learning with Scarce Annotations", arXiv
Install requirements: the environement used to run this code is provided in environment.yml. It can be installed using conda with the following command (environment name will be salsa):
conda env create -f environment.yml
python rotNet.py --dataset mydataset --network mynetwork --save_dir myrotnetdir
Choose any network among: {ResNet-18, RevNet-18,TempEns} and dataset in {cifar10, cifar100, svhn}
python alternative_training.py --dataset mydataset --save_dir mydir --rotnet_dir myrotnetdir --nb_labels_per_class 10
Default parameters are for CIFAR10. For CIFAR100 use inner milestones [14,20], for SVHN use learning rate of 0.1 and outer milestones [120,150].
A sample training script to run the same experiments 10 time with different dataset splits is available in scripts/. You will have to specify (in the following order) dataset, number of labels per class, save_dir, and rotnet_dir.
For instance: sh ./scripts/train_semi.sh cifar10 10 mydir myrotnetdir
python supervised_training.py --dataset mydataset --network mynetwork --save_dir mydir --rotnet_dir myrotnetdir
This code supports CIFAR10, CIFAR100 and SVHN datasets.
We also provide the script to generate the two moons figure of the paper (Fig 1.). To generate the pictures run python two_moons/pi_model.py, figures will be available in the folder render/.
If you use this code for your project please consider citing us:
@article{rebuffi2019semi,
title={Semi-Supervised Learning with Scarce Annotations},
author={Rebuffi, Sylvestre-Alvise and Ehrhardt, Sebastien and Han, Kai and Vedaldi, Andrea and Zisserman, Andrew},
journal={Technical report},
year={2019}
}