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arXiv · 1603.08575

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
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Abstract

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. We further show that the networks produce accurate inferences when compared to supervised counterparts, and that their structure leads to improved generalization.

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S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, Geoffrey E. Hinton. 2016-03-28. Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. https://arxiv.org/abs/1603.08575

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