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Pose CycleGAN

Architecture

Vanilla Generator and Discriminator

The code - Vanilla CycleGAN.

Discriminator with PatchGAN

The code - CycleGAN with PatchGAN

Generator with MLP (Contrastive Learning)

The code - CycleGAN with PatchNCE loss

Segment Loss

The code - CycleGAN with segment loss

Dataset

Download the chosen dataset:

  • apple2orange
  • horse2zebra (buggy: some images don't have the RGB channel)
./download_dataset apple2orange
├── datasets                   
|   ├── <dataset_name>         # i.e. apple2orange
|   |   ├── train              # Training
|   |   |   ├── A              # Contains domain A images (i.e., Apple)
|   |   |   └── B              # Contains domain B images (i.e., Orange)
|   |   └── test               # Testing
|   |   |   ├── A              # Contains domain A images (i.e., Apple)
|   |   |   └── B              # Contains domain B images (i.e., Orange)

Usage

Training

python train.py --dataset apple2orange --cuda --n_epochs 20 --decay_epoch 10

Testing

python test.py --dataset apple2orange --cuda

Generate

python generate.py --input_img pix/surf.jpg --generator A2B
python generate.py --input_img pix/nude.jpg --generator B2A

Losses

References

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