COMMUTE-GAN: COMPTETITIVE MULTIPLE EFFI-CIENT GENERATIVE ADVERSARIAL NETWORKS

Abstract

In complex creative scenarios, co-creativity by multiple agents offers great advantages. Each agent has a specific skill set and a set of abilities, which is generally not enough to perform a 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. Single generators also have to be very large so that they can generalize complex datasets, so this method can easily run into memory constraints. The current 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 competitive multiple efficient GANs (ComMutE-GANs), with multiple generators and a single critic/discriminator, without introducing external complexities such as a classifier model. We introduce a new component to the generator loss during GAN training, based on the Total Variation Distance (TVD). Our method offers a robust, stable, memory efficient and easily parallelizable architecture. 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 compare ComMutE-GANs with larger single-generator GANs and show its memory efficiency and increased accuracy.

1. INTRODUCTION

With respect to human beings, "Creators" refer to any and all who engage in creative thinking. When people learn about new topics, they create cognitive structures that allow them to understand the topics; they generate concepts that are new to them, although possibly already very well known to others. This is creativity at a strictly intra-personal level. When working in a social setting, such as a company or a classroom, one has to broaden this horizon to include "Co-creativity"."Creativity through collaboration" summarizes the definition of co-creativity as defined by Lubart (2017). Often, collaborators have different or complementary skills that enable them to frequently produce shared creations that they could not or would not produce on their own (Lubart & Thornhill-Miller, 2020) . AI-aided co-creation has also been proven to improve general well-being (Yu et al., 2021) . Generative Adversarial Nets (GANs) are implicit generative models where one or more generators play a zero-sum game with a discriminator to recreate and potentially expand a chosen dataset. According to the definition established above, the generator models in modern day GANs such as those described in the works of Karras et al. ( 2018 2017) exhibit creativity on an intra-personal level. Accordingly, generative networks have been applied in many creative applications such as painting (Ganin et al., 2018; Mellor et al., 2019; Parikh & Zitnick, 2020 ), doodling (Ha & Eck, 2017; Cao et al., 2019) and extending fictional languages (Zacharias et al., 2022) . Most noticeable, in all the applications listed above, a single, large, generative agent was applied to perform a complex task rather than breaking it down into smaller, more easily manageable sub-tasks. This approach, although effective, is upper-bounded by memory constraints. Inspiration



); Sauer et al. (2022); Karras et al. (2020); Goodfellow et al. (2014); Radford et al. (2015); Arjovsky et al. (2017); Gulrajani et al. (

