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As a computer vision researcher it must be pain you to see that all your learnings are for nought when faced with deep learning methods which can get amazing pe
by marshallp 15y ago
As a computer vision researcher it must be
pain you to see that all your learnings are
for nought when faced with deep learning
methods which can get amazing performances
from raw pixels (see mnist results for
example). Also see ronan collobert's natural
language processing from scratch paper where
handily beats the past few decades of nlp
research in parsing (in terms of efficiency,
and probably performances soon too). Or see
the microsoft research speech recognition
swork which has beaten out all previous by
a significant margin using deep learning.
- apu 15y agoNot at all! I'd love for vision to be solved, no matter what the method. I'm more than happy to move onto another field if that's the case. But I don't think it is. MNIST data is not particularly challenging. It's great that deep learning methods work there -- they must be doing something right. Come back and taunt me when deep learning methods start getting state-of-the-art results on, e.g., Pascal VOC: http://pascallin.ecs.soton.ac.uk/challenges/VOC/ http://pascallin.ecs.soton.ac.uk/challenges/VOC/
- marshallp 15y agogetting best results on the harder vision challenges is simply a matter of let the computers run long enough. Collobert's work for example took 3 months of training. I don't see why vision challenges should any different. Perhaps the vision researchers, of which there are many more people than the few deep learning groups should try it.