MarginGAN
This repository is the implementation of the paper "MarginGAN: Adversarial Training in Semi-Supervised Learning".
1."preliminary" is the implementation of Preliminary Experiment on MNIST of the paper. Thank the authors of pytorch-generative-model-collections and examples of pytorch, our code is widely adapted from their repositories.
To train the network, an example is as follows:
python main.py \
--gan_type MarginGAN \
--num_labels 600 \
--lrC 0.1 \
--epoch 50
2."ablation" is the implementation of Ablation Study on MNIST of the paper. To train the network, an example is as follows:
python main.py \
--gan_type MarginGAN_UG \
--num_labels 600 \
--lrC 0.01 \
--epoch 50
3."further" is the implementation of Experiment on SVHN and CIFAR-10 of the paper. Thank the authors of mean teacher, our code is widely adapted from their repositories.
To train the network, an example is as follows:
python MarginGAN_main.py \
--dataset cifar10 \
--train-subdir train+val \
--eval-subdir test \
--batch-size 128 \
--labeled-batch-size 31 \
--arch cifar_shakeshake26 \
--consistency-type mse \
--consistency-rampup 5 \
--consistency 100.0 \
--logit-distance-cost 0.01 \
--weight-decay 2e-4 \
--lr-rampup 0 \
--lr 0.05 \
--nesterov True \
--labels data-local/labels/cifar10/1000_balanced_labels/00.txt \
--epochs 180 \
--lr-rampdown-epochs 210 \
--ema-decay 0.97 \
--generated-batch-size 32
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