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, assistant professor of data science, presented a paper titled "Structured Prediction Using cGANs with Fusion Discriminator" at the 2019 International Conference for Learning Representations (ICLR) in New Orleans.

Johnson collaboratedÌýwith colleagues from Johns Hopkins University on the research, which they presented at the Workshop on Deep Generative Models for Structured Prediction at ICLR, the preeminent international conference for research on learning representations, a subfield of machine learning and artificial intelligence.Ìý

The research has applications in computer vision, and the group are also working on a related project to use structured prediction to improve the classification of skin lesions.

An alumnus of the University of Ò×ʤ²©¹ÙÍø, Johnson leads the bachelor's degree program at Ò×ʤ²©¹ÙÍø Manchester.

Dr. Johnson is an alumnus of the University of Ò×ʤ²©¹ÙÍø, earning his Ph.D in mathematics in 2010.

An abstract of the paper is below, and you can .

We propose the fusion discriminator, a single unified framework for incorporating conditional information into a generative adversarial network (GAN) for a variety of distinct structured prediction tasks, including image synthesis, semantic segmentation, and depth estimation. Much like commonly used convolutional neural network - conditional Markov random field (CNN-CRF) models, the proposed method is able to enforce higher-order consistency in the model, but without being limited to a very specific class of potentials. The method is conceptually simple and flexible, and our experimental results demonstrate improvement on several diverse structured prediction tasks.