Mode collapse

Mode collapse is a problem that refers to a situation in which the generator network generates samples that have little variety or when a model starts generating the same images. Sometimes, a probability distribution is multimodal and very complex in nature. This means that it might contain data from different observations and that it might have multiple peaks for different sub-graphs of samples. Sometimes, GANs fail to model a multimodal probability distribution of data and suffer from mode collapse. A situation in which all the generated samples are virtually identical is known as complete collapse.

There are many methods that we can use to overcome the mode collapse problem. These include the following:

• By training multiple models (GANs) for different modes

• By training GANs with diverse samples of data