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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
by opticalflow 10y ago
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...
- argonaut 10y agoYou seem to be describing hyperparameter optimization, which I seriously doubt CNNs are going to be used for anytime soon.
- opticalflow 10y agoJust curious -- why would you think not?
- pedrosorio 10y agoIt has been done already: http://arxiv.org/abs/1502.05700 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.