> but isn't that the whole point of GANs? not quite, but youre on the right path. think about it this way: you (the generative model) are trying to predict a

> but isn't that the whole point of GANs? not quite, but youre on the right path. think about it this way: you (the generative model) are trying to predict a unit gaussian, which is just a fancy way to say bell curve. you get +1 if you predict a number in this distribution (eg 0.1 or -0.5, which is within one standard deviation of the mean of 0); you get -1 if you predict a number thats "far" from this distribution (something like 40 - which has an infinitesimally low probability of being drawn from a unit gaussian). mode collapse, then, is when you predict 0 all the time. yes, you are technically correct but youve failed to learn the true distribution. obviously ive simplified this quite a bit and have anthropomorphize the model, but i hope you get the gist. otherwise, the [original paper](https://arxiv.org/abs/1406.2661 https://arxiv.org/abs/1406.2661) is refreshingly easy to read.