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Question to anyone that knows this area in depth. I'm assuming that this impressive feat has a disadvantage (over the traditional example intensive technique o
by ac2u 9y ago
Question to anyone that knows this area in depth.
I'm assuming that this impressive feat has a disadvantage (over the traditional example intensive technique of training a CNN with something like ImageNet) in the form of taking a long time to generate the corrected image.
With that assumption in mind, could this new technique be reversed? As in feeding in sharp images, getting back out corrupted ones, for the purposes of generating data sets where there isn't much data to begin with?
You could then take that data and use it to train a more traditional CNN to sort of amortize the results of the technique in the paper and have the process happening faster.
- wonderous 9y agoDo you mean generative adversarial networks? https://en.m.wikipedia.org/wiki/Generative_adversarial_network https://en.m.wikipedia.org/wiki/Generative_adversarial_netwo...
- ac2u 9y agoNo not really, this isn't generation for the sake of classification. It's generation in order to generate a dataset to train a new network that exhibits the characteristics of the network in the paper that can be used quickly and in a more general fashion.
- wonderous 9y agoMeaning that your assumption is that there is an optimal generalized network structure for generate a neural network as described in the paper and that as such, what is missing from the research is how to create an optimal way to generate a malformed source/target seed. Is that correct?
- ac2u 9y agoI'm not suggesting it's missing from the research as I don't have anywhere near the area of expertise to make that call. But you're correct in your summary: I'm wondering if that's a possible follow up.