Message Passing Multi-Agent Generative Adversarial Networks

Arnab Ghosh*, Viveka Kulharia*, Vinay Namboodiri

*Equal contribution

The Crux

Interesting Differences in Generations by the two Generators

Generations of Generator 1 with uniform(-1,1) noise distribution with conditioned message passing. It captures detailed facial expressions.

Generations of Generator 2 with N(0,1) noise distribution with conditioned message passing. It captures smooth features of facial expression.

Artistic Creation

Generations look as if showing the process of artist creation. The generations were created using a message interpolation by keeping the noise constant and varying the message between the 2 messages. It shows that the message space is also continuous.

The Best Performing Model

Salient Features

Results and Analysis

Classification Results

Model Discriminator Rep Message Rep Msg+Disc Rep
DCGAN Radford et al. (2015) 22.48% NA NA
Improved GANs Salimans et al. (2016) 8.11 ± 1.3 % NA NA
Different Noise MP 20.1% 53.48% 18.7%
Different Noise CMP 17.1% 54.21% 15.2%
Conceding CMP 18.37% 64.46% 17.4%
Competing CMP 17.76% 52.05% 16.8%
Competing Objective 18.02% NA NA
Conceding Objective 17.56% NA NA

Message Clustering

Some Interesting Interpolation Results

This is a perfect rendering of the creation that an artist takes in order to create a masterpiece applying changes one layer at a time

The generations move from a cartoon-like representation of the woman to the actual face of the woman.

The generations move from a human to angel/spirit like representation

The classic example of depiction of ageing process. It depicts the various stages of aging of a person.