MaNoise

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

Robust training of neural networks under noisy labels

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

Abstract

The performance of supervised learning methods highly depends on the quality of the labels. However, accurately labeling a large number of datasets is a time-consuming task, which sometimes results in mismatched labeling. When the neural networks are trained with noisy data, it might be biased to the noisy data. Therefore the performance of the neural networks could be poor. While label noise has been widely studied in the machine learning society, only a few studies have been reported to identify or ignore them during the process of training. In this project, we will investigate the way to train the neural network under noisy data robustly. In particular, we will focus on exploring effective learning strategies and loss correction methods to address the problem.

Requirements:

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

Location:

  • Garching

References:


ProjectForm
Title: Robust training of neural networks under noisy labels
Abstract: The performance of supervised learning methods highly depends on the quality of the labels. However, accurately labeling a large number of datasets is a time-consuming task, which sometimes results in mismatched labeling. When the neural networks are trained with noisy data, it might be biased to the noisy data. Therefore the performance of the neural networks could be poor. While label noise has been widely studied in the machine learning society, only a few studies have been reported to identify or ignore them during the process of training. In this project, we will investigate the way to train the neural network under noisy data robustly. In particular, we will focus on exploring effective learning strategies and loss correction methods to address the problem.
Student: Cagri Yildiz
Director: Prof. Dr. Nassir Navab
Supervisor: Dr. Seong Tae Kim
Type: Master Thesis
Area: Machine Learning
Status: running
Start:  
Finish:  
Thesis (optional):  
Picture:  


Edit | Attach | Refresh | Diffs | More | Revision r1.3 - 14 Jan 2020 - 16:13 - SeongTaeKim