2020
CVPR
CVPR 2020
SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation
Abstract
We propose a novel method for combining synthetic and real images when training networks to determine geometric information from a single image. We suggest a method for mapping both image types into a single, shared domain. This is connected to a primary network for end-to-end training. Ideally, this results in images from two domains that present shared information to the primary network. Our experiments demonstrate significant improvements over the state-of-the-art in two important domains, surface normal estimation of human faces and monocular depth estimation for outdoor scenes, both in an unsupervised setting.
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Interdisciplinary Bridge
— Computer Vision and Deep Learning and Machine Learning
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Hot Topic Early Bird
— monocular depth estimation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Application Areas > Domain Adaptation
Computer Vision > Analysis > Depth Estimation
Deep Learning > Learning Types > Self-Supervised Learning
Deep Learning > Learning Types > Unsupervised Learning
Deep Learning > Learning Types > Domain Adaptation