  Training of Generative Adversarial Network (GAN) on a video dataset is a
challenge because of the sheer size of the dataset and the complexity of each
observation. In general, the computational cost of training GAN scales
exponentially with the resolution. In this study, we present a novel memory
efficient method of unsupervised learning of high-resolution video dataset
whose computational cost scales only linearly with the resolution. We achieve
this by designing the generator model as a stack of small sub-generators and
training the model in a specific way. We train each sub-generator with its own
specific discriminator. At the time of the training, we introduce between each
pair of consecutive sub-generators an auxiliary subsampling layer that reduces
the frame-rate by a certain ratio. This procedure can allow each sub-generator
to learn the distribution of the video at different levels of resolution. We
also need only a few GPUs to train a highly complex generator that far
outperforms the predecessor in terms of inception scores.
