Having nice conversations with Generative Adversarial Networks (GANs)

I recently came across a blog by Bram Cohen and read a very interesting post (which can be found HERE) about one of the most intriguing flaws current neural networks have (so to speak): why they fail so tremendously (in some cases, under some circumstances) when a small amount of graciously created noise is introduced into the input data.

He particularly recommended reading the paper:

Adi Shamir, Itay Safran, Eyal Ronen, Orr Dunkelman
"A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance"
https://arxiv.org/abs/1901.10861

Look at (the text surrounding and) Figure 1 (What the h...?)

That is the reason why I find particularly interesting the research approaches that Prof. Dr. Antonio Torralba and his PhD student David Bau are carrying out in a particular type of neural networks called Generative Adversarial Networks.

Not the same, but, (well) in some sense...

A summarising video of what I mean by this would be the one below: