Detecting Mode Drop and Collapse in GANs Using Simplified Frèchet Distance

Chung-Il Kim, Seungwon Jung, Jihoon Moon, Eenjun Hwang

http://doi.org/10.5626/JOK.2019.46.10.1012

Even though generative adversarial network (GAN) is an excellent model for generating data based on the estimation of real data distribution by of two adversarial learning network, this model often suffers from mode drop that does not learn distribution during learning, or mode collapse that generates only one or very few distribution samples. Most studies to detect these problems have used well-balanced data or additional neural network models. In this paper, we propose a method to detect mode drop and collapse by using a simplified Frèchet distance, which does not require any additional model or well-balanced data. Through various experiments, we showed that our proposed distance metric detected mode drop and collapse more accurately than any other metrics used in GANs.