Explainable and interpretable models in computer vision and machine learning

2018

Abstract

Research progress in computer vision and pattern recognition has led to a variety of modelling techniques with (almost) human-like performance in a variety of tasks. A clear example of this type of models is neural networks, whose deep variants dominate the arenas of computer vision among other fields. Although this type of models has obtained astounding results in a variety of tasks (eg face recognition), they are limited in their explainability and interpretability. That is, in general, users cannot say too much about:

Authors

Sergio Escalera
Isabelle Guyon
Isabelle Guyon
Xavier Baró
Sathyanarayanan N. Aakur
Umut Güçlü
Marcel van Gerven