Training the cGAN for face aging is a three-step process:
- Training the cGAN
- Initial latent vector approximation
- Latent vector optimization
We will cover these steps one by one in the following sections.
Kailash Ahirwar is a machine learning and deep learning enthusiast. He has worked in many areas of Artificial Intelligence (AI), ranging from natural language processing and computer vision to generative modeling using GANs. He is a co-founder and CTO of Mate Labs. He uses GANs to build different models, such as turning paintings into photos and controlling deep image synthesis with texture patches. He is super optimistic about AGI and believes that AI is going to be the workhorse of human evolution.
Read more about Kailash Ahirwar
Training the cGAN for face aging is a three-step process:
We will cover these steps one by one in the following sections.
This is the first step of the training process. In this step, we train the generator and the discriminator networks. Perform the following steps:
# Define hyperparameters
data_dir = "/path/to/dataset/directory/"
wiki_dir = os.path.join(data_dir, "wiki_crop")
epochs = 500
batch_size = 128
image_shape = (64, 64, 3)
z_shape = 100
TRAIN_GAN = True
TRAIN_ENCODER = False
TRAIN_GAN_WITH_FR = False
fr_image_shape = (192,...
Kailash Ahirwar is a machine learning and deep learning enthusiast. He has worked in many areas of Artificial Intelligence (AI), ranging from natural language processing and computer vision to generative modeling using GANs. He is a co-founder and CTO of Mate Labs. He uses GANs to build different models, such as turning paintings into photos and controlling deep image synthesis with texture patches. He is super optimistic about AGI and believes that AI is going to be the workhorse of human evolution.
Read more about Kailash Ahirwar