Title: CoGANs: Collaborative Generative Adversarial Networks. Abstract: In complex creative scenarios, co-creativity by multiple agents offer great advantages. Each agent has a specific skill set and a set of abilities, which is sometimes not enough to perform a general, large and complex task single-handed.
These kinds of tasks benefit substantially from collaboration. In deep learning applications, data generation is an example of such a complex, potentially multi-modal task. Previous Generative Adversarial Networks (GANs) focused on using a single generator to generate multi-modal datasets, which is sometimes known to face issues such as mode-collapse and failure to converge. The multi-generator based works such as MGAN, MMGAN, MADGAN and AdaGAN either require training a classifier online, the use of complex mixture models or sequentially adding generators, which is computationally complex.
  In this work, we present a simple, novel approach of training collaborative GANs (CoGAN), with multiple generators and a single critic/discriminator, without introducing external complexities such as a classifier model. We show that this method of workload division meets the state-of-the-art quality metrics, and makes GAN training robust. We present a proof-of-concept on the MNIST dataset, which has 10 modes of data. The individual generators learn to generate different digits from the distribution, and together learn to generate the whole distribution. We introduce a new component to the generator loss during GAN training, based on the Total Variation Distance (TVD) and show that it significantly improves stability during training and performance over state-of-the-art single generator GANs.