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> So if they were to let the training go on for too long the network will produce an exact copy of the distorted input image. It won't. The objective (min (E(x
by syrak 9y ago
> So if they were to let the training go on for too long the network will produce an exact copy of the distorted input image.
It won't. The objective (min (E(x, x0) + R(x)) they are trying to optimize (over output images x) amounts to a combination of:
- The output image x should "look like" the input image x0, this is the error term E(x, x0);
- The output image x should be "regular", this is the regularization term R(x).
The latter term prevents overfitting: R should be chosen such that noisy images for example are considered irregular (high R(x)).
- flyingspork 9y agoAh you're totally right about it not producing an exact copy, they even mention how they used different error functions for the different classes of distortion, my mistake. But w.r.t. the regularization term, what I thought they meant by "we replace the regularizer R(x) with the implicit prior captured by the neural network" at the end of pg 2 was that they let the natural behavior of a neural network during optimization serve as the regularization, without need for an explicit regularization term. Not entirely sure though.