  Generative Adversarial Networks (GANs) have become predominant in image
generation tasks. Their success is attributed to the training regime which
employs two models: a generator G and discriminator D that compete in a minimax
zero sum game. Nonetheless, GANs are difficult to train due to their
sensitivity to hyperparameter and parameter initialisation, which often leads
to vanishing gradients, non-convergence, or mode collapse, where the generator
is unable to create samples with different variations. In this work, we propose
a novel Generative Adversarial Stacked Convolutional Autoencoder(GASCA) model
and a generative adversarial gradual greedy layer-wise learning algorithm
de-signed to train Adversarial Autoencoders in an efficient and incremental
manner. Our training approach produces images with significantly lower
reconstruction error than vanilla joint training.
