Tag: gan

Abstract: Generative adversarial networks (GANs) have shown tremendous promise in learning to generate data and effective at aiding semi-supervised classification. However, to this point, semi-supervised GAN methods make the assumption that the unlabeled data set contains only samples of the joint distribution of the classes of interest, referred to as inliers. Consequently, when presented with […]

Congratulations to our labmates Jose Ruiz-Munoz and Alina Zare as well as collaborators Jyothier Nimmagadda, Tyler Dowd and James Baciak!  Their paper, titled “Super Resolution for Root Imaging”, was recently published to Applications in Plant Sciences (APPS). If you’re interested in learning about a super-resolution framework for enhancing images of plant roots by using convolutional […]

Congratulations to our labmates Jose Ruiz-Munoz and Alina Zare as well as collaborators Jyothier Nimmagadda, Tyler Dowd and James Baciak!  Their paper, titled “Super Resolution for Root Imaging”, was recently accepted to Applications in Plant Sciences (APP). If you’re interested in learning about a super-resolution framework for enhancing images of plant roots by using convolutional […]

Abstract: High-resolution cameras have become very helpful for plant phenotyping by providing a mechanism for tasks such as target versus background discrimination, and the measurement and analysis of fine-above-ground plant attributes. However, the acquisition of high-resolution (HR) imagery of plant roots is more challenging than above-ground data collection. Thus, an effective super-resolution (SR) algorithm is […]