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.