ABSTRACT
Scene representation-the process of converting visual sensory data into concise descriptions-is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them.
Subject(s)
Machine Learning , Neural Networks, Computer , Vision, OcularABSTRACT
Patients in this study showed remarkable improvement when 1.5% lidocaine and 0.3% nifedipine were applied twice daily for 6 weeks. This extremely safe, well tolerated, and effective treatment should provide family physicians with a reliable nonsurgical method for treating chronic anal fissures.
