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Poster
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
S. M. Ali Eslami · Nicolas Heess · Theophane Weber · Yuval Tassa · David Szepesvari · koray kavukcuoglu · Geoffrey E Hinton

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #53
in Wednesday Posters »

We present a framework for efficient inference in structured image models that explicitly reason about objects. We achieve this by performing probabilistic inference using a recurrent neural network that attends to scene elements and processes them one at a time. Crucially, the model itself learns to choose the appropriate number of inference steps. We use this scheme to learn to perform inference in partially specified 2D models (variable-sized variational auto-encoders) and fully specified 3D models (probabilistic renderers). We show that such models learn to identify multiple objects - counting, locating and classifying the elements of a scene - without any supervision, e.g., decomposing 3D images with various numbers of objects in a single forward pass of a neural network at unprecedented speed. We further show that the networks produce accurate inferences when compared to supervised counterparts, and that their structure leads to improved generalization.

Keywords: Deep Learning or Neural Networks · (Application) Computer Vision · (Application) Object and Pattern Recognition · (Other) Probabilistic Models and Methods · Variational Inference · Graphical Models

Author Information

S. M. Ali Eslami (Google DeepMind)
Nicolas Heess (Google DeepMind)
Theophane Weber (DeepMind)
Yuval Tassa (Google DeepMind)
David Szepesvari (Google DeepMind)
koray kavukcuoglu (Google DeepMind)
Geoffrey E Hinton (Google)

Geoffrey Hinton received his PhD in Artificial Intelligence from Edinburgh in 1978 and spent five years as a faculty member at Carnegie-Mellon where he pioneered back-propagation, Boltzmann machines and distributed representations of words. In 1987 he became a fellow of the Canadian Institute for Advanced Research and moved to the University of Toronto. In 1998 he founded the Gatsby Computational Neuroscience Unit at University College London, returning to the University of Toronto in 2001. His group at the University of Toronto then used deep learning to change the way speech recognition and object recognition are done. He currently splits his time between the University of Toronto and Google. In 2010 he received the NSERC Herzberg Gold Medal, Canada's top award in Science and Engineering.

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