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We approach structured output prediction by optimizing a deep value network(DVN) to precisely estimate the task loss on different output configurationsfor a given input. Once the model is trained, we perform inference by gradientdescent on the continuous relaxations of the output variables to find outputswith promising scores from the value network. When applied to imagesegmentation, the value network takes an image and a segmentation mask asinputs and predicts a scalar estimating the intersection over union between theinput and ground truth masks. For multi-label classification, the DVN'sobjective is to correctly predict the F1 score for any potential labelconfiguration. The DVN framework achieves the state-of-the-art results onmulti-label prediction and image segmentation benchmarks.

Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs