I am still somewhat a novice in the ML world, but I had a strange idea about CNNs and wanted to ask if this would be a valid way to check the robustness of a general CNN that classifies certain images.
Let's say that I make a CNN that takes in many different images of sports players performing a certain action (basketball shot, football kick, freestyle in swimming, flip in gymnastics, etc). Firstly, would it be possible for such a CNN to distinguish between such varied images and classify them accurately? And if so, can it be a good idea to compare this "larger" CNN to multiple "smaller" more specialized ones that take in images from one particular sport?
In other words, I want to know that if I have a "larger" CNN that gives me an output like "football being kicked", is there a way to then double-check that output with a smaller CNN that only focuses on football moves? In essence, could we create a system where once you obtain an output from a general CNN, it automatically classifies the same image through a more specialized CNN, and then if the results are of similar accuracy, you know for sure that CNN works?
Kind of like having a smaller CNN as a "ground-truth" for the bigger one? In my head it kind of goes like this:
large_net_output = 'Football kick identified with 95.56% confidence' for sport in large_net: if sport == 'football': access = small_net_for_football return small_net_for_football_output elif sport == 'swimming': access = small_net_for_swimming return small_net_for_swimming_output elif sport == 'baseball': access = small_net_for_baseball return small_net_for_baseball_output # and so on.... >>> small_net_for_football_output = 'Football kick identified with 97.32% confidence' robustness_check = large_net_output - small_net_for_football_output print(robustness_check) >>> 'Your system is accurate within a good range of 1.76%'
I hope this makes sense, and that this question does not cause any of your to cringe. Would appreciate any feedback on this!