How to determine if problem is solvable by neural network? I want to identify subtle patterns in images using a convolutional neural net. I have seen several examples where people gave up reasoning that the pattern is not dominant or consistent enough to be picked up by a neural network. Since tuning these networks is very time consuming I wonder: What are methods to validate that the problem is solvable/the pattern can be recognised (or at least give an indication of the same)? 

Background: My problem

My goal is to identify subtle differences in products based on their images. I want to break down a product image into a large variety of describing attributes. Now I need to find out which product attributes are well suited for identification. 

For example, I want to distinguish collar types in clothing images. Therefore I need to figure out if the human understanding of collar types (waterfall, V-shaped, round etc.) are distinct and consistent enough to be properly identified by a neural net with human level performance. 

Of course images always differ a little and the solvability is clearly related to the data available. But before collecting and cleaning hundreds of thousands of images I would like to find out if results will be any good.
Neural network will pick up on any patterns as long as your environment is not fully stochastic.

Here is what authors of Using Recurrent Neural Networks to Frecasting of Forex used to determine the amount of stochasticity:

page 7, bottom. "Hurst Exponent".