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A path to unsupervised learning through adversarial networks

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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.

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A path to unsupervised learning through adversarial networks Sky89 <Sky89@sky68.com> - 2018-04-22 22:39 -0400

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