1 research outputs found
Host-Pathongen Co-evolution Inspired Algorithm Enables Robust GAN Training
Generative adversarial networks (GANs) are pairs of artificial neural
networks that are trained one against each other. The outputs from a generator
are mixed with the real-world inputs to the discriminator and both networks are
trained until an equilibrium is reached, where the discriminator cannot
distinguish generated inputs from real ones. Since their introduction, GANs
have allowed for the generation of impressive imitations of real-life films,
images and texts, whose fakeness is barely noticeable to humans. Despite their
impressive performance, training GANs remains to this day more of an art than a
reliable procedure, in a large part due to training process stability.
Generators are susceptible to mode dropping and convergence to random patterns,
which have to be mitigated by computationally expensive multiple restarts.
Curiously, GANs bear an uncanny similarity to a co-evolution of a pathogen and
its host's immune system in biology. In a biological context, the majority of
potential pathogens indeed never make it and are kept at bay by the hots'
immune system. Yet some are efficient enough to present a risk of a serious
condition and recurrent infections. Here, we explore that similarity to propose
a more robust algorithm for GANs training. We empirically show the increased
stability and a better ability to generate high-quality images while using less
computational power.Comment: 8 pages, 10 figure