Applying Explanatory Methods on Convolutional Neural Networks

Konvolucijske neuronske mreže su poznate po njihovim nevjerojatnim postignućima u području računalnog vida, ali kao modeli crne kutije pate od manjka transparentnosti. Postoje metode objašnjivosti koji pokušavaju dati uvide u metodologiju koju mreže koriste kada stvaraju zaključke. Ove metode su veoma subjektivne, i ne postoji objektivni, opće prihvačeni način za mjerenje njihove korisnosti. Metode su detaljno objašnjene, s opisima o napretku koji su ostvarili kroz vrijeme. Metode su zatim korištene i uspoređene na raznim skupinama podataka, s naglaskom na njihovu primjenjivost, pronicljivost, i lakoću korištenja. Convolutional neural networks are known for being capable of increadible feats in computer vision, but as a black box model they suffer from a lack of transparency. There are explanatory methods that attempt to give insights into the inference methodology of these networks. These methods are highly subjective in nature, and there is no objective, universally agreed way to measure their usefulness. These methods are described in detail, with descriptions on the progress that has been made on their improvement over time. The methods are then used and compared on various datasets, with attention given to their applicability, insightfulness, and ease of use.