Robust training of neural networks under noisy labels
Supervision: Prof. Dr. Nassir Navab,
Dr. Seong Tae Kim,
Dr. Shadi Albarqouni
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:
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