At a high level (ignoring many details) the main idea is to replace generator networks in GANs with Restricted Boltzman Machines, or RBMs, which are easier to train (more stable). The authors call this kind of architecture "Boltzmann Encoded Adversarial Machines," or BEAM for short.
The experiments provide persuasive evidence that BEAMs outperform GANs. Figure 3, in particular, I find very persuasive -- it compares the ability of different architectures to learn to generate low-dimensional mixtures of Gaussians, with BEAMs very clearly outperforming GANs. The results in higher-dimensional applications such as image generation also suggest that BEAMs outperform GANs, but the improvement is somewhat more subjective due to the nature of high-dimensional data. Obviously, these results need to be replicated by others.
It looks promising to me. That said, it's been years since I've touched an RBM -- I only have a vague recollection of how they work and how they're trained, layer by layer, as proposed by Hinton in 2006 or so. Time to re-read old papers!
The problem, in BEGAN's case, is that when your idea of similarity is based of mean squared error, high frequency details are just not important. [1] You can see this by doing PCA on natural image patches. BEGAN uses an autoencoder trained on MSE.
RBMs produce blurry images because the architecture is not good at representing multiplicative interactions. You just get splodges of colour.
[1] http://danielwaterworth.com/posts/what's-wrong-with-autoenco...
I'm sure there's plenty of interesting work being done in ML to improve on this situation and come up with new architectures. Yet I was moderately surprised when I rediscovered Boltzmann machines recently, and found not much work seemed to be going on there at all (very little at NIPS 2017 for example?).
This BEAM seems intriguing, here's hoping it opens the door to a better understanding and modeling of our world.
A lot of the RAM, for instance, is DDR2 and usually a measly 1gb apiece. They take up the exact same amount of space as RAM with 4gb apiece or more. I don’t know entirely why I still have them. Now that I’m doing physical computing/IoT development, Im seeing how pointless it is to have a bunch of desktops/laptops when I can get much more done - conveniently I might add - with a teeny tiny RedBear microcontroller.
I think an inherent feature of technology is having to get used to the idea that things age and die much faster than other products. Whether that’s physical hardware or trained neural networks, there comes a point when we just have to let go.
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models https://arxiv.org/abs/1703.09912
RBMs are natural candidates for models which handle these kind of issues quite well. 1. Although people have done work trying to get GANs to work well with multimodal data, it's pretty kludgy. 2. GANs do not provide a means of inference (contrast VAEs which can satisfy this demand). 3. We have built a solid extension of RBMs to temporal models which work quite well.
However, as explained in this paper, stock RBMs have significant training issues. This paper attempts to improve the situation.
