pytorch-VAT

This is an unofficial pytorch implementation of a paper, Distributional Smoothing with Virtual Adversarial Training [Miyato+, ICLR2016].

Please note that this is an ongoing project.

Requirements

• Python 3.5+
• PyTorch 0.4
• TorchVision
• click

Usage

Train MNIST classifier with only labeled data (100 images)

CUDA_VISIBLE_DEVICES=<gpu_id> python train_baseline.py --n_label 100

Error rate: about 30%

VAT with mixture of labeled and unlabeled data

CUDA_VISIBLE_DEVICES=<gpu_id> python train_vat.py --n_label 100

Error rate: about 2%

References

• [1]: T. Miyato et al. "Distributional Smoothing with Virtual Adversarial Training", in ICLR, 2016.