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Neural networks have shown to be a practical way of building a very complexmapping between a pre-specified input space and output space. For example, aconvolutional neural network (CNN) mapping an image into one of a thousandobject labels is approaching human performance in this particular task. Howeverthe mapping (neural network) does not automatically lend itself to other formsof queries, for example, to detect/reconstruct object instances, to enforcetop-down signal on ambiguous inputs, or to recover object instances fromocclusion. One way to address these queries is a backward pass through thenetwork that fuses top-down and bottom-up information. In this paper, we show away of building such a backward pass by defining a generative model of theneural network's activations. Approximate inference of the model wouldnaturally take the form of a backward pass through the CNN layers, and itaddresses the aforementioned queries in a unified framework.

A backward pass through a CNN using a generative model of its activations