instruction:
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
question:
We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution. This metric, which we call mini-batch energy distance, combines optimal transport in primal form with an energy distance defined in an adversarially learned feature space, resulting in a highly discriminative distance function with unbiased mini-batch gradients. Experimentally we show OT-GAN to be highly stable when trained with large mini-batches, and we present state-of-the-art results on several popular benchmark problems for image generation.
answer:
An extension of GANs combining optimal transport in primal form with an energy distance defined in an adversarially learned feature space.


question:
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.
answer:
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. 


question:
We show that if the usual training loss is augmented by a Lipschitz regularization term, then the networks generalize.   We prove generalization by first establishing a stronger convergence result, along with a rate of convergence.    A second result resolves a question posed in Zhang et al. (2016): how can a model distinguish between the case of clean labels, and randomized labels?   Our answer is that Lipschitz regularization using the Lipschitz constant of the clean data makes this distinction.   In this case, the model learns a different function which we hypothesize correctly fails to learn the dirty labels.  
answer:

We prove generalization of DNNs by adding a Lipschitz regularization term to the training loss. We resolve a question posed in Zhang et al. (2016).