New method for addressing the reliability challenges of neural networks in inverse imaging problems

Uncertainty estimation is critical to improving the reliability of deep neural networks. A research team led by Aydogan Ozcan at the University of California, Los Angeles, has introduced an uncertainty quantification method that uses cycle consistency to enhance the reliability of deep neural networks in solving inverse imaging problems.

california challenges imaging los angeles machine learning & ai networks neural networks quantification reliability research research team team uncertainty university