IMLE (implicit maximum likelihood estimation) as far as I can tell is a trivial method of parameterizing a random variable distribution and tuning it to make true data (e.g., image) examples more likely. The technique relies on finding nearest neighbor example images, which in turn needs a metric of image distance. Original IMLE uses least-squares pixel distance for example, which is not a very flexible or effective metric in practice (eg., it is completely confused by rotation).
The whole advantage of GaN is it does NOT need an explicit distance metric for comparing images--instead the discriminator effectively learns the metric in order to improve its ability to distinguish real images from generated/fake ones. Arguably this is the whole advantage of GaNs.
So to argue that IMLE can solve mode collapse is a false equivalency.
The whole advantage of GaN is it does NOT need an explicit distance metric for comparing images--instead the discriminator effectively learns the metric in order to improve its ability to distinguish real images from generated/fake ones. Arguably this is the whole advantage of GaNs.
So to argue that IMLE can solve mode collapse is a false equivalency.