This repo contains code for training ComixGAN. It is trained using COCO dataset and ~1000 comic images obtained by extracting keyframes from some cartoons.
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Prepare third dataset with comic images with blurred edges. Steps:
- Detect edge pixels using a standard Canny edge detector.
- Dilate the edge regions.
- Apply a Gaussian smoothing in the dilated edge regions.
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Split dataset to training, validation and testing:
- Testing: Get 5k of random coco images as final test dataset
- For point 4: 5k COCO validation images. All the remaining images used for training.
- For point 5:
- COCO: 5k validation images. All the remaining images use for training.
- Comic: 100 validation images.
- Comic blurred: 100 validation images.
- All the remaining images are used for training.
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Training in batches using 384x384 images.
- Sample images in batches
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Pretrain Generator to make it return output the same with the input (On whole COCO dataset).
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Pretrain Discriminator on COCO and comic images.
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Adversarial Training and tuning of D and G:
- Loss that consists of Adversarial Loss (including smoothed edges part) + Content Loss with weight=10
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Test on 5k final test dataset.
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Add Callbacks (Weights saving [x], history saving [x])
- Non-saturating loss for Generator
- Generator/Discriminator ration during adversarial training: 3:1 (3 G weights updates for 1 D update)
- Pretrained discriminator
- Sigmoid in last D layer
