A simple GAN and a DCGAN are similar in that they both use a generative and a discriminative models , to generate synthetic images.
A simple GAN uses fully-connected layers in both G and D, whereas a DCGAN uses convolutional layers. This means that a DCGAN is able to capture spatial dependencies in the data, which can improve the quality of the generated images.
DCGAN is typically trained using batch normalization and transposed convolutional layers, which can stabilize the training process and improve the performance of the generative model.
pip install -r requirements.txt
