A path to unsupervised learning through adversarial networks

[Sky89]

Hello,

Read this:


A path to unsupervised learning through adversarial networks

Adversarial networks provide a strong algorithmic framework for building
unsupervised learning models that incorporate properties such as common
sense, and we believe that continuing to explore and push in this
direction gives us a reasonable chance of succeeding in our quest to
build smarter AI.

However, generative adversarial networks (GANs) were previously thought
to be unstable. Sometimes the generator never started learning or
producing what we would perceive to be good generations. At Facebook AI
Research (FAIR), we've published a set of papers on stabilizing
adversarial networks in collaboration with our partners, starting with
image generators using Laplacian Adversarial Networks (LAPGAN) and Deep
Convolutional Generative Adversarial Networks (DCGAN), and continuing
into the more complex endeavor of video generation using Adversarial
Gradient Difference Loss Predictors (AGDL). Regardless of what kinds of
images or videos we gave to these systems, they would start learning and
predict plausible scenarios of the world.


Read more here:

https://code.facebook.com/posts/1587249151575490/a-path-to-unsupervised-learning-through-adversarial-networks/



Thank you,
Amine Moulay Ramdane.