vat_tf
Tensorflow implementation for reproducing the semi-supervised learning results on SVHN and CIFAR-10 dataset in the paper "Virtual Adversarial Training: a Regularization Method for Supervised and Semi-Supervised Learning" http://arxiv.org/abs/1704.03976
Requirements
tensorflow-gpu 1.1.0, scipy 0.19.0(for ZCA whitening)
Preparation of dataset for semi-supervised learning
On CIFAR-10
python cifar10.py --data_dir=./dataset/cifar10/
On SVHN
python svhn.py --data_dir=./dataset/svhn/
Semi-supervised Learning without augmentation
On CIFAR-10
python train_semisup.py --dataset=cifar10 --data_dir=./dataset/cifar10/ --log_dir=./log/cifar10/ --num_epochs=500 --epoch_decay_start=460 --epsilon=10.0 --method=vat
On SVHN
python train_semisup.py --dataset=svhn --data_dir=./dataset/svhn/ --log_dir=./log/svhn/ --num_epochs=120 --epoch_decay_start=80 --epsilon=2.5 --top_bn --method=vat
Semi-supervised Learning with augmentation
On CIFAR-10
python train_semisup.py --dataset=cifar10 --data_dir=./dataset/cifar10/ --log_dir=./log/cifar10aug/ --num_epochs=500 --epoch_decay_start=460 --aug_flip=True --aug_trans=True --epsilon=8.0 --method=vat
On SVHN
python train_semisup.py --dataset=svhn --data_dir=./dataset/svhn/ --log_dir=./log/svhnaug/ --num_epochs=120 --epoch_decay_start=80 --epsilon=3.5 --aug_trans=True --top_bn --method=vat
Semi-supervised Learning with augmentation + entropy minimization
On CIFAR-10
python train_semisup.py --dataset=cifar10 --data_dir=./dataset/cifar10/ --log_dir=./log/cifar10aug/ --num_epochs=500 --epoch_decay_start=460 --aug_flip=True --aug_trans=True --epsilon=8.0 --method=vatent
On SVHN
python train_semisup.py --dataset=svhn --data_dir=./dataset/svhn/ --log_dir=./log/svhnaug/ --num_epochs=120 --epoch_decay_start=80 --epsilon=3.5 --aug_trans=True --top_bn --method=vatent
Evaluation of the trained model
On CIFAR-10
python test.py --dataset=cifar10 --data_dir=./dataset/cifar10/ --log_dir=<path_to_log_dir>
On SVHN
python test.py --dataset=svhn --data_dir=./dataset/svhn/ --log_dir=<path_to_log_dir> --top_bn