[D] Text. The dream....

Ten years from now, in a perfect, liberal world, the machines have taken over 50% of the most time-consuming tasks, they can't reason or adapt to anything but their programming. We fill the gaps with human creation and ingenuity, we have the Machine-Learning Assisted Renaissance. We start focusing on the questions that matter. Why are we not brothers? Why are all the resources going to the top? This is the most exciting time to be alive that has ever existed, because either we accept our faith to run into the great filter or plan with consideration to the future generations of our planet.

[D] Reddit Meetup @ CVPR. Saturday, July 22nd at 5pm - 6:30. Samsung room 323C, Honolulu convenction center, third floor. (Cross-post from r/computervision )

Please upvote for visibility. For those of you attending CVPR (or lucky enough to live here) please join us for an informal Reddit meetup! Meet on Saturday, July 22nd at 5pm. Room 323C (Samsung meeting room) RSVP here: https://goo.gl/forms/40ArnIftckBATXIm1 (headcount estimation, no name required) Edit: I can't spell. Convention center has no harmful convection, radiation or conduction. Humans will be safe.

[D] What are some solid academic reasons for using Generative Adversarial Networks?

Hi all, So one of the major topics in machine learning is Generative Adversarial Networks. I'd like to find out some solid academic motivations for why there's so much interest in this. Obviously we generate new samples but are these good enough to supplement smaller datasets? I also realise the discriminator can be used for downstream tasks such as classification if adapted post original training. What about networks such as InfoGAN? I'm sorry if this seems like a vague question but I'm trying to grasp why so much interest is being shown in these, admittedly cool, networks.