"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 designed to train Adversarial Autoencoders in an efficient and incremental manner. Our training approach produces images with significantly lower reconstruction error than vanilla joint training."