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Neural scene representation and rendering / Generative Query Network
This session we will discuss "Neural scene representation and rendering" ( Generative Query Network) There will be a brief introduction to the paper followed by discussion. Please re-read World Models Abstract ( ) and bring whatever related ideas to discuss. --- ## Abstract Scene representation – the process of converting visual sensory data into concise descriptions – is a requirement for intelligent behaviour. Recent work has shown that neural networks excel at this task when provided large labelled datasets. However, removing the reliance on human labelling 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 towards machines that autonomously learn to understand the world around them.

Mission Hall UCSF

1589, 1599 4th St · San Francisco, CA

What we're about

Deep learning is a rapidly growing field with dozens of new publications each week on Arxiv. This group is a time set aside to go over interesting research from the previous week. We'll pick a one or a few papers to read and discuss.

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