Abstract
We propose an inference procedure for deep convolutional neural networks (CNNs) when partial evidence is
available. Our method consists of a general feedback-based
propagation approach (feedback-prop) that boosts the prediction accuracy for an arbitrary set of unknown target labels when the values for a non-overlapping arbitrary set
of target labels are known. We show that existing models trained in a multi-label or multi-task setting can readily
take advantage of feedback-prop without any retraining or
fine-tuning. Our feedback-prop inference procedure is general, simple, reliable, and works on different challenging visual recognition tasks. We present two variants of feedbackprop based on layer-wise and residual iterative updates. We
experiment using several multi-task models and show that
feedback-prop is effective in all of them. Our results unveil
a previously unreported but interesting dynamic property of
deep CNNs. We also present an associated technical approach that takes advantage of this property for inference
under partial evidence in general visual recognition tasks