Gagan Narang
Papers
1
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1
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About
Dr. Gagan Narang is a leading researcher at the intersection of computer vision, generative AI, and infrastructure safety. His work centers on developing advanced deep learning methods for visual inspection and predictive maintenance of critical infrastructure, including bridges, dams, and tunnels. Dr. Narang’s most notable contribution is his pioneering work on controllable object inpainting through Generative Adversarial Networks (GANs), specifically his paper "COIGAN: Controllable Object Inpainting Through Generative Adversarial Network for Defect Synthesis in Data Augmentation." This research addresses a critical bottleneck in AI-driven robotic inspection: the scarcity of labeled defect data. By generating realistic, controllable synthetic defects, his method significantly enhances the robustness of visual inspection models, directly improving the safety and reliability of infrastructure monitoring. With his work already garnering attention in the field, Dr. Narang is recognized for bridging the gap between generative AI and real-world engineering challenges, making autonomous inspection systems more practical and effective in preventing catastrophic failures.
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