
Neural networks with Van Gogh's artistic talent - kounine
https://www.tastehit.com/blog/neural-networks-with-artistic-talent/
======
brudgers
The process where

    
    
      f(photo, art) -> hybrid
    

fed

    
    
      f(f(photo,art), photo)
    

illustrates the one way nature of neural nets and indicates that that these
may be much much harder to reason about and debug than in classic cases of
concurrency with shared state.

~~~
hcburger
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 don't understand exactly how the neural net
works. We can't really be sure there won't be an unexpected failure case.

2) This is a good thing, because we don't HAVE to understand how the neural
network works. We had no idea how to solve the problem, but we just threw
loads of data at a neural network, and it solved the problem.

The "correct" interpretation probably depends on the situation/problem. That
being said, my former colleagues and myself made some attempts, with limited
success, in understanding neural networks trained for some specific problems.

E.g. in this paper [http://www.cv-
foundation.org/openaccess/content_cvpr_2013/pa...](http://www.cv-
foundation.org/openaccess/content_cvpr_2013/papers/Schuler_A_Machine_Learning_2013_CVPR_paper.pdf)
in section 5, we made an attempt to understand how a neural network trained
for image deblurring really works.

And in this paper
[http://arxiv.org/pdf/1211.1552.pdf](http://arxiv.org/pdf/1211.1552.pdf)
starting in section 4, we provide a much more in depth analysis regarding
neural networks trained for image denoising.

Utlimately, our analyses "fail" in the sense that we cannot put into simple
words how the neural networks work. However, we are successful in gaining
limited insight into how the neural networks operate.

