arXiv:1211.1552cs.CV
Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms
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Abstract
Image denoising can be described as the problem of mapping from a noisy image to a noise-free image. In another paper, we show that multi-layer perceptrons can achieve outstanding image denoising performance for various types of noise (additive white Gaussian noise, mixed Poisson-Gaussian noise, JPEG artifacts, salt-and-pepper noise and noise resembling stripes). In this work we discuss in detail which trade-offs have to be considered during the training procedure. We will show how to achieve good results and which pitfalls to avoid. By analysing the activation patterns of the hidden units we are able to make observations regarding the functioning principle of multi-layer perceptrons trained for image denoising.
I'm the author of the blog post. I think you're right in saying that neural nets are hard to reason about. It's probably impossible to put into words exactly how a deep neural network achieves its results. There are two ways to interpret this: 1) This is a bad thing, because we…
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