MaPerturb

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Investigation of Interpretation Methods for Understanding Deep Neural Networks

Supervision: Prof. Dr. Nassir Navab, Dr. Seong Tae Kim

Abstract

Machine learning and deep learning has made breakthroughs in many applications. However, the basis of their predictions is still difficult to understand. Attribution aims at finding which parts of the network’s input or features are the most responsible for making a certain prediction. In this project, we will explore the perturbation-based attribution methods.

Requirements:

  • Good understanding of statistics and machine learning methods.
  • Very good programming skills in Python & TensorFlow? / PyTorch?

Location:

  • Garching

ProjectForm
Title: Investigation of Interpretation Methods for Understanding Deep Neural Networks
Abstract: Machine learning and deep learning has made breakthroughs in many applications. However, the basis of their predictions is still difficult to understand. Attribution aims at finding which parts of the network’s input or features are the most responsible for making a certain prediction. In this project, we will explore the perturbation-based attribution methods.
Student:  
Director: Prof. Dr. Nassir Navab
Supervisor: Dr. Seong Tae Kim
Type: Project
Area:  
Status: running
Start:  
Finish:  
Thesis (optional):  
Picture:  


Edit | Attach | Refresh | Diffs | More | Revision r1.1 - 20 Apr 2020 - 12:53 - SeongTaeKim