Deep Convolutional GAN learns to generate MNIST digits
Deep Convolutional GANs use Conv2d layers in the discriminator and ConvTranspose2d layers in the generator for upsampling. The generator learns to produce realistic images from random noise, while the discriminator learns to distinguish real from fake.
This architecture uses strided convolutions instead of pooling layers, and batch normalization for stable training.
