Awesome, thanks for the link. I had not seen it before, but I would not consider myself an expert (just yet  ).
It certainly feels like a very different use of adversarial:
So you have a training set of (sample, source, label) and you want to produce an encoding e = e(sample) that keeps as much information about label as possible but such that source is as independent from (encoding, label) as possible and the idea is that this helps when classification you have new sources.
Interesting. Do you have a use case in mind? I wonder if it could be used for advanced style fingerprinting, too, when you switch source and label.
Best regards
Thomas
[edit: fixed the error Ajay pointed out below]