Thesis Details
Interpretace konvolučních neuronových sítí
The aim of this work was to compare several methods for visualizing the features of each class on the input pixel layer of the CNN. Each method uses a different algorithm, based on gradients, to compute the resulting values. Using the implementation of each method, the resultant values of the methods are obtained by using the equation of energy concentration. The resultant values are presented in tables and graphs from which the success rate of the result of the work can be read. The difference between the methods and comparison of their results can be read from the work. This makes it possible to get an overview of gradient based visualization methods.
CNN, NN, MLP, gradient, preceptron, sigmoid, bias, threshold, weights, hook
Bařina David, Ing., Ph.D. (DCGM FIT BUT), člen
Burget Radek, doc. Ing., Ph.D. (DIFS FIT BUT), člen
Češka Milan, doc. RNDr., Ph.D. (DITS FIT BUT), člen
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
@bachelorsthesis{FITBT23910, author = "Daniel Kamenick\'{y}", type = "Bachelor's thesis", title = "Interpretace konvolu\v{c}n\'{i}ch neuronov\'{y}ch s\'{i}t\'{i}", school = "Brno University of Technology, Faculty of Information Technology", year = 2021, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/23910/" }