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Comment by f9us98a | Hacker News Reader

It gets the best results for what? Maybe for classification and recognition.

But the point of the article is that there is more to computer vision. Stereo, optical flow, geometry, and physics can only be aided by deep learning so much.

Another point not mentioned is the computational power required for deep learning. Consider programming the physics for a ball rolling down an incline. You could use (1) the math itself, vs. (2) a neural net. It's clear that the direct math approach could be orders of magnitude faster than neural nets. I wouldn't be surprised if directly coding the physics would achieve 1,000x the performance of a neural net.

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I would posit that a deep learning network that learns to optimize parameters for a complex algorithm outside the convolutional network itself may have immense utility outside the classification problem. Call it a marriage of classic computer vision with deep CNN, or a hybrid approach. I don't think it's a binary decision. A deep CNN can find the optimal parameters (once trained) for a classic CV problem for a given image or video or other dataset, like superresolution, patch-based inpainting, or motion tracking. The training is the most computationally intensive part. As someone with way too many kids, I can testify...

You seem to be describing hyperparameter optimization, which I seriously doubt CNNs are going to be used for anytime soon.

Just curious -- why would you think not?

It has been done already: http://arxiv.org/abs/1502.05700

Edit: Although what they seem to describe is replacing GPs with neural networks in Bayesian optimization which is supposedly more efficient.

Since the point of Bayesian optimization is to limit the number of times you have to evaluate a new set of hyperparameters, I am not sure how useful it is to "be able to scale" (i.e. even if maintaining the GP is O(n^3) with the number of evaluations, the costly part should be to evaluate the hyperparameters in the first place) but I haven't read the paper so they may show some high dimensional hyperparameter cases where performing a lot of evaluations pays off.

The point of all of those though are toward the goal of machine/computer vision which is "understand the physical world through [images]."

If training CNNs does it faster/better than having a better understanding of Stereopsis/etc... then who cares?

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