Computer Vision, Deep Learning, Generative Adversarial, Generator, Gans.
architecture, the input to the generator is a noise vector z of size 100. This is first projected and reshaped to 1024x4x4 data followed by convolution layers of 512x8x8, 256x16x16 and 128x32x32. Finally this is fed into the output layer to generate a 64x64 RGB image. Example DCGAN generator