ConditionalGAN.md

Conditional Generative Adversarial Network (cGAN)

A Conditional Generative Adversarial Network (cGAN) is an extension of the traditional GAN framework where additional conditioning information, typically in the form of class labels or embeddings, is provided to both the generator and the discriminator. This allows for the generation of samples conditioned on specific attributes or classes, enhancing control over the generated outputs.

Parameters

Parameter	Description	Default	Choices
--train	train model	False
--sample	sample from model	False
--dataset	Dataset name	mnist	mnist, cifar10, fashionmnist, chestmnist, octmnist, tissuemnist, pneumoniamnist, svhn, tinyimagenet, cifar100, places365, dtd, imagenet
--no_wandb	Disable Wandb	False
--batch_size	batch size	128
--n_epochs	number of epochs	100
--lr	learning rate	0.0002
--beta1	beta1	0.5
--beta2	beta2	0.999
--latent_dim	latent dimension	100
--n_classes	number of classes	10
--img_size	image size	32
--channels	channels	1
--sample_and_save_freq	sample interval	5
--checkpoint	checkpoint path	None
--n_samples	number of samples	9
--d	number of initial filters	128
--num_workers	Number of workers for Dataloader	0

You can find out more about the parameters by checking util.py or by running the following command on the example script:

python CondGAN.py --help

Training

The training process is similar to the one mentioned in VanillaGAN.md, but with the inclusion of the aforementioned embeddings.

python CondGAN.py --train --dataset mnist --n_classes 10

Sampling

The sampling process is also similar but it requires to also include the class-related embedding and not only a noisy latent sample:

python CondGAN.py --train --dataset mnist --n_classes 10 --checkpoint ./../../models/ConditionalGAN/CondGAN_mnist.pt