Creative divergent synthesis with generative models

TL;DR

This paper explores how generative models can be trained to produce more creative outputs by diverging from their original data distribution, introducing a new training objective called Bounded Adversarial Divergence (BAD).

Contribution

It proposes a novel training framework and objective, BAD, to enable generative models to exhibit creative divergence from data distributions.

Findings

Preliminary results demonstrate the potential of BAD for creative divergence.

The approach offers new perspectives on training generative models for creative tasks.

Abstract

Machine learning approaches now achieve impressive generation capabilities in numerous domains such as image, audio or video. However, most training \& evaluation frameworks revolve around the idea of strictly modelling the original data distribution rather than trying to extrapolate from it. This precludes the ability of such models to diverge from the original distribution and, hence, exhibit some creative traits. In this paper, we propose various perspectives on how this complicated goal could ever be achieved, and provide preliminary results on our novel training objective called \textit{Bounded Adversarial Divergence} (BAD).