Visualizing GAN-based Extreme Learned Image Compression

  • Systems based on Artificial Intelligence are thrusting their way into evermore fields of application, with the potential to globally revolutionize working and living environments over coming decades. Especially in highly sensitive use-cases with lots of responsibility, like medicine, law enforcement or military, it is of utmost importance to be able to fully comprehend why and how an artificially intelligent program acts. The field of Explainable AI addresses just this task, developing techniques to gain insight into otherwise hard to interpret machine learning models. Knowledge gained through these explanations can also be used to debug and improve any existing models from all kinds of application fields. In this thesis, several appropriate Explainable AI techniques are used to investigate a Deep Compression System for images based on Generative Adversarial Networks.

Volltext Dateien herunterladen

Metadaten exportieren

Metadaten
Verfasserangaben:Daniel Habermayr
URN:urn:nbn:de:bvb:860-opus4-2990
DOI:https://doi.org/10.57688/299
Gutachter/Betreuer:Andreas Siebert
Dokumentart:Bachelorarbeit
Sprache:Englisch
Jahr der Fertigstellung:2021
Veröffentlichende Institution:Hochschule für Angewandte Wissenschaften Landshut
Titel verleihende Institution:Hochschule für Angewandte Wissenschaften Landshut
Datum der Freischaltung:27.01.2022
Freies Schlagwort / Tag:Deep Compression; Explainable AI; Generative Adversarial Network
GND-Schlagwort:Künstliche IntelligenzGND
Seitenzahl:34
Fakultät / Institut:Fakultät Informatik
Lizenz (Deutsch):Keine Creative Commons Lizenz (es gilt das deutsche Urheberrecht)