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Bullshit... this is still encoding. It does not matter that it is using neural network to encode frame. Guys, when you get over this neural network hype? I can
by klakier 10y ago
Bullshit... this is still encoding. It does not matter that it is using neural network to encode frame. Guys, when you get over this neural network hype?
I can as well create and encoder using some machine learning, to create a blurry and inferior version of the movie. But what's the point?
I could as well used other prediction method with some kind of memory, much superior and nobody will enjoy it.
Summing up: nothing impressive here.
- l3m0ndr0p 10y agoI agree. I was wondering why this is such a big deal. When a "neural networking" computer has a flash of inspiration and creates a screen play for a movie, and creates a virtual world based on that screen play. Then we'll have a something to talk about.
- Mithaldu 10y agoSee my explanation for why this matters here: https://news.ycombinator.com/item?id=11830140 https://news.ycombinator.com/item?id=11830140
- l3m0ndr0p 10y agoOH. Thank you. Now that makes a bit more sense & it's looks like an impressive feat for data compression.
- tacos 10y agoAn explanation written by someone who actually did it a decade ago: https://www.quora.com/What-is-the-potential-of-neural-networks-in-data-compression https://www.quora.com/What-is-the-potential-of-neural-networ...
- zodiac 10y ago> But what's the point? Autoencoders don't really have much practical use now, but that's not the point of them. The point is we really want to figure out how to do unsupervised learning well and autoencoders are one of the few ways of doing it. We want to do unsupervised learning well because most learning that humans do is unsupervised. The idea behind autoencoding is that by forcing the network to try to learn efficient ways to compress the data, it could learn important features of the data. The fact that the pictures are so blurry means that this doesn't work very well, but that's why it's a research problem. Autoencoders don't work well, but some unsupervised techniques that extend on them do, and we get impressive results like https://arxiv.org/pdf/1511.06434.pdf https://arxiv.org/pdf/1511.06434.pdf (see page 5) where the network learns to generate natural-looking bedroom images.
- klakier 10y agoThanks for great answer!
- kastnerkyle 10y agoNote that DCGAN (or standard GAN generally) doesn't have an encode path (or any other way to easily condition generation) so it is not well suited to this particular task. The image quality is stellar though, and I linked to several recent papers above which combine a GAN approach with an explicit encode. This should allow them to be used for the kinds of things in this link.
- sp332 10y agoThe author's own blog https://medium.com/@Terrybroad/autoencoding-blade-runner-88941213abbe https://medium.com/@Terrybroad/autoencoding-blade-runner-889... makes it clearer that this is primarily an art project. It's supposed to make you think about how minds understand things, not to be a superior encoder.