We propose a novel  adversarial learning framework in this work. Existing adversarial learning methods involve two separate networks, i.e., the structured prediction models and the discriminative models, in the training. The information captured by discriminative models complements that in the structured prediction models, but few existing researches have studied on utilizing such information to improve structured prediction models at the inference stage. In this work, we propose to refine the predictions of structured prediction models by effectively integrating discriminative models into the prediction. Discriminative models are treated as energy-based models. Similar to the adversarial learning, discriminative models are trained to estimate scores which measure the quality of predicted outputs, while structured prediction models are trained to predict contrastive outputs with maximal energy scores. In this way, the gradient vanishing problem is ameliorated, and thus we are able to perform inference by following the ascent gradient directions of discriminative models to refine structured prediction models. The proposed method is able to handle a range of tasks, \emph{e.g.}, multi-label classification and image segmentation.   Empirical results on these two tasks validate the effectiveness of our learning method. This work focuses on applying adversarial learning BID9 to solve structured prediction tasks, e.g., multi-label classification and image segmentation. Adversarial learning can be formalized as a minimax two-player game between structured prediction models and discriminative models. Discriminative models are learned to distinguish between the outputs predicted by the structured prediction models and the training data, while structured prediction models are learned to predict outputs to fool discriminative models. Though structured prediction models are trained by the gradients of discriminative models, existing methods rarely use discriminative models to improve structured prediction models at the inference stage.A straightforward way of utilizing discriminative models for inference is to follow the ascent gradient directions of discriminative models to refine the predicted outputs. However, due to the wellknown gradient vanishing problems, the gradients of discriminative models are almost zero for all the predicted outputs. It is difficult to resolve the gradient vanishing problems, because they are caused by the training instability of the existing adversarial learning framework. Consequently, most existing methods do not use the information from discriminative models to refine structured prediction models.Most existing adversarial learning methods take discriminative models as classifiers. If discriminative models well separate real and predicted samples, they tend to assign the same scores to all predicted samples. Differently, energy-based models BID21 BID12 usually predict different energy scores for different samples. Therefore, we propose to train discriminative models as energy-based models to ameliorate the gradient vanishing problems. In our framework, discriminative models are learned to assign scores to evaluate the quality of predicted outputs. Structured prediction models are learned to predict outputs that are judged to have maximum scores by discriminative models. In this way, discriminative models are trained to approximate continues value functions which evaluate the quality of predicted output. The gradients of discriminative models are not zero for predicted outputs. Thus, we can refine structured prediction models by following the ascent gradient directions of discriminative models at the inference stage. In this paper, we refer our method as learning discriminative models to refine structured prediction models (LDRSP).The proposed method learns discriminative models utilizing the data generated by the structured prediction models. BID12 found that the key to learning deep value networks is generating proper training data. We propose to augment the training set of discriminative models by following the data generation methods proposed in BID12 . At the training stage, we simultaneously run the inference algorithm to generate extra training samples utilizing models in previous iterations. These samples are useful since they are generated along the gradient-based inference trajectory utilized at the inference stage. We also augment the training set with adversarial samples BID10 . These samples are used as negative samples to train the discriminative models.To validate our method, experiments are conducted on multi-label classification, binary image segmentation, and 3-class face segmentation tasks, and experimental results indicate that our method can learn discriminative models to effectively refine structured prediction models.This work has two contributions: (1) We propose a novel adversarial learning framework for structured prediction, in which the information captured by discriminative models can be used to improve structured prediction models at the inference stage. (2) We propose to learn discriminative models to approximate continues value functions that evaluate the quality of the predicted outputs, and thus ameliorate the gradient vanishing problems. This paper proposes a novel learning framework, in which discriminative models are learned to refine structured prediction models. Discriminative models are trained as energy-based models to estimate scores that measure the quality of generated samples. Structured prediction models are trained to predict contrastive samples with maximum energy scores. Once models are learned, we perform inference by following the ascent gradient directions of discriminative models to refine structured prediction models. We apply the proposed method to multi-label classification and image segmentation tasks. The experimental results indicate that discriminative models learned by the proposed methods can effectively refine generative models. As the future work, we will explore different ways to generate extra training samples and apply our method to more challenging tasks.
We propose a novel adversarial learning framework for structured prediction, in which discriminative models can be used to refine structured prediction models at the inference stage. 
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