Safe-Student

[CVPR 2022]Safe-Student for Safe Deep Semi-Supervised Learning with Unseen-Class Unlabeled Data

Introduction

This is the code for paper(CVPR2022) "Safe-Student for Safe Deep Semi-Supervised Learning with Unseen-Class Unlabeled Data".

Setups

The codebase is implemented using Python and PyTorch.

To replicate our environment and ensure seamless execution of the code, we recommend rebuilding the environment using the provided environment.yaml file. This can be done using the following Conda command:

conda env create -f environment.yaml

Fremawork

For a quick overview of the SAFE-STUDENT framework, refer to the framework diagram included in this repository:

Running SAFE-STUDENT for benchmark dataset

Here is an example.

Step 1: Teacher Pretraining

python teacher_pretrain.py --model WideResnet --dataset CIFAR10 --n_labels 2400 --n_unlabels 20000 --n_valid 5000 --n_class 6 --ratio 0.6

Step 2: Safe Student Training and Testing

python safe_student_CIFAR10.py --model WideResnet --tau_1 0.6 --tau_2 0.5 --dataset CIFAR10 --n_labels 2400 --n_unlabels 20000 --n_valid 5000 --n_class 6 --ratio 0.6 --name CIFAR10_06 --ts_iteration 3