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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

Area 5+6+7+8 #53

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


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.

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