
A path to unsupervised learning through adversarial networks - dwaxe
https://code.facebook.com/posts/1587249151575490/a-path-to-unsupervised-learning-through-adversarial-networks/?utm_source=codedot_rss_feed&utm_medium=rss&utm_campaign=RSS+Feed
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AJ007
Interesting timing with relation to OpenAI's post on GANs last week
[https://openai.com/blog/generative-
models/](https://openai.com/blog/generative-models/)

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daveguy
That and their recent release of their Go engine, Dark Forest:
[https://news.ycombinator.com/item?id=11922864](https://news.ycombinator.com/item?id=11922864)

I wonder if morale is low at Facebook AI with these two, "Hey, us too!"
announcements.

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Houshalter
Adversarial networks are super cool. The only theoretical issue I have with
them, is how do you measure their quality or how much they are overfitting?
The generative net could learn to produce perfect copies of images from the
training set, and that would optimally satisfy the loss function.

Of course the advantage of adversarial nets is they can use the unlimited
amounts of training data you can find on the internet, so overfitting isn't a
huge concern. But still, unlike normal machine learning, you can't make a
validation set and test how well they are performing on it. And that limits
the ability to set their hyperparameters optimally.

