3 ms·
Remember fractal image compression ? https://youtu.be/AjdogjBxfco?t=260 https://youtu.be/AjdogjBxfco?t=260
by vbuwivbiu 9y ago
Remember fractal image compression ?
https://youtu.be/AjdogjBxfco?t=260 https://youtu.be/AjdogjBxfco?t=260
- cztomsik 9y agoThx for that!
- eitland 9y agoUpvoted you both. I normally really don't like videos but this was worth the few minutes it took. (The link points to the middle of a 9 min video.)
- ska 9y agoThis can also be looked at as the original source of patch based denoising, etc. In the end it's about capturing the scaling properties and self similarity of natural images. This is also, for example, why wavelets were so effective as a basis. David Mumford particularly did some great work on this sort of thing a couple of decades ago, along with many others. I hope when people are rushing around trying to apply convolutional nets to everything they aren't losing these insights.
- fjsolwmv 9y agoThe benefit of CNNs is like the benefits of SVMs -- they generalize all the great old techniques so you don't have to understand them all, you just throw more CPU at the optimization problem.
- ska 9y agoI don't think that's true particularly in this case. This paper is pointing out that you can encode a structural prior in a CNN - but knowing the "great old techniques" will help you design the right network architecture to do that. SVMs were a surprise when they came out, no so much a generalization as a challenge (at least at first)