Sky89 <Sky89@sky68.com>: Apr 22 10:39PM -0400 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. |
Sky89 <Sky89@sky68.com>: Apr 22 09:53PM -0400 Hello, Read this: "Predictive Learning" is the New Buzzword in Deep Learning Read more here: https://medium.com/intuitionmachine/predictive-learning-is-the-key-to-deep-learning-acceleration-93e063195fd0 Thank you, Amine Moulay Ramdane. |
You received this digest because you're subscribed to updates for this group. You can change your settings on the group membership page. To unsubscribe from this group and stop receiving emails from it send an email to comp.programming.threads+unsubscribe@googlegroups.com. |
No comments:
Post a Comment