The annual Israel Machine Vision Conference (IMVC) took place on March 6, 2018 at David InterContinental Tel Aviv.
Dr. Michal Holtzman Gazit spoke at a conference on “Balanced Unsupervised Style Transfer using Generative Adversarial Networks”.
Generative adversarial networks (GAN) have recently shown major progress in generating images, as in cross-model generation or applying a specific style to images. We utilize this progress in order to improve generation of simulated images required for computer vision tasks. Our objective function is based on principles acquired from recently published work: preserving key attributes between the input and the translated image; balancing the power of the discriminator against the generator in order to better achieve Nash equilibrium; and using a task-dedicated loss in order to ensure that the generated images are valuable for the desired task at hand.
Michal Holtzman Gazit is a senior computer vision researcher in Rafael LTD since 2013, with nearly 20 years of experience in the field of computer vision and image processing. She received her BSc. (1998) and MSc. (2004) in Electrical Engineering Technion, and her PhD (2010) in Computer Science, Technion. During 2010-2012, she was a post-doctorate fellow in the computer science department in the University of British Columbia, Vancouver, Canada.
Her main research interests are computer vision, image processing, and deep learning.
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