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implementation of the paper "MarginGAN: Adversarial Training in Semi-Supervised Learning"
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ablation
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preliminary
NeurIPS19_poster_MarginGAN_v1_read_only.pdf
README.md

README.md

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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