Saw this one at the Theoretical Physics for Deep Learning workshop at ICML this year.

Abstract: We humans seem to have an innate understanding of the asymmetric progression of time, which we use to efficiently and safely perceive and manipulate our environment. Drawing inspiration from that, we address the problem of learning an arrow of time in a Markov Decision Process. We illustrate how a learned arrow of time can capture meaningful information about the environment, which in turn can be used to measure reachability, detect side-effects and to obtain an intrinsic reward signal. We show empirical results on a selection of discrete and continuous environments, and demonstrate for a class of stochastic processes that the learned arrow of time agrees reasonably well with a known notion of an arrow of time given by the celebrated Jordan-Kinderlehrer-Otto result.

https://openreview.net/pdf?id=SkevntbkJB

Abstract

An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. In contrast to previous approaches like the “neural gas” method of Martinetz and Schulten (1991, 1994), this model has no parameters which change over time and is able to continue learning, adding units and connections, until a performance criterion has been met. Applications of the model include vector quantization, clustering, and interpolation.

PDF: http://papers.nips.cc/paper/893-a-growing-neural-gas-network-learns-topologies.pdf

Interactive demo (2017): https://www.demogng.de/

GANs are usually trained on pretty high variance datasets (Imagenet, CIFAR, etc). Intuitively, these datasets are hard to model – they encompass a wide range of classes, and even within these classes, there’s a ton of variance between and within images.

A lower variance distribution, however, seems like it would be a strictly easier distribution to model. It seems natural to me that if I had a low variance dataset, I could take off-the-shelf GAN architectures and parameters that are able to model high-variance datasets, plug in my own data, and get nice outputs. For instance, let’s say I had thousands of overhead views of a crop field, which changed in minor ways based on the season, crop quality, etc – I’m intuiting that the variance of this data would be much smaller and consequently easier to model (this is not my actual problem – it’s even lower variance between+within images – but let’s just use this as an example).

I’m finding that in practice, however, this isn’t really true. Something about modeling this low variance dataset is proving to be hard for these off-the-shelf GANs. It could be a quality of the dataset aside from the low-variance, but some other tests I’ve run (artificially increasing the variance of the images) have me thinking otherwise.

I’m thinking that perhaps the methods/architectures to optimize GAN training are probably different across different dataset types – specifically, low vs high variance datasets. Any thoughts?