About the Speaker:

Gustavo Carneiro is a Professor of the School of Computer Science at the University of Adelaide, ARC Future Fellow, and the Director of Medical Machine Learning at the Australian Institute of Machine Learning. He joined the University of Adelaide as a senior lecturer in 2011, has become an associate professor in 2015 and a professor in 2019. In 2014 and 2019, he joined the Technical University of Munich as a visiting professor and a Humboldt fellow. From 2008 to 2011 Dr. Carneiro was a Marie Curie IIF fellow and a visiting assistant professor at the Instituto Superior Tecnico (Lisbon, Portugal) within the Carnegie Mellon University-Portugal program (CMU-Portugal). From 2006 to 2008, Dr. Carneiro was a research scientist at Siemens Corporate Research in Princeton, USA. In 2005, he was a post-doctoral fellow at the the University of British Columbia and at the University of California San Diego. Dr. Carneiro received his Ph.D. in computer science from the University of Toronto in 2004. His main research interest are in the fields of computer vision, medical image analysis and machine learning.


Noisy Labels are commonly present in data sets automatically collected from the internet, mislabelled by non-specialist annotators, or even specialists in a challenging task, such as in the medical field. Although deep learning models have shown significant improvements in different domains, an open issue is their ability to memorise noisy labels during training, reducing their generalisation potential. As deep learning models depend on correctly labelled data sets and label correctness is difficult to guarantee, it is crucial to consider the presence of noisy labels for deep learning training. In this talk, I will talk about approaches that have been proposed in the literature to improve the training of deep learning models in the presence of noisy labels, and new ideas that have been proposed by my group.

Upcoming Speakers


Minh Ha

RIKEN Center for Advanced Intelligence Project, Tokyo

Title: TBD

Date/Time: Friday, Sep 10 2021 - 10:00 am (GMT + 7)