Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

23 Mar 2016

The Attend-Infer-Repeat Architecture

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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    author = {S. M. Ali Eslami and Nicolas Heess and Theophane Weber and 
    		  Yuval Tassa and Koray Kavukcuoglu and Geoffrey E. Hinton}, 
    title = {Attend, Infer, Repeat: Fast Scene Understanding with Generative Models},
    year = {2016}