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:
   Metric embeddings are   immensely useful representations of associations between entities   (images, users, search queries, words, and more).   Embeddings are learned by  optimizing a loss objective of the general form of a sum over example associations. Typically, the optimization uses stochastic gradient updates over minibatches of examples that are arranged  independently at random. In this work, we propose the use of {\em structured arrangements} through randomized {\em microbatches} of examples that are more likely to include similar ones. We make a principled argument for the properties of our arrangements  that accelerate the training and present efficient algorithms to generate microbatches that respect the marginal  distribution of training examples.   Finally, we observe experimentally that our structured arrangements accelerate training by 3-20\%. Structured arrangements emerge as a powerful and novel performance knob for SGD that is independent and complementary to other SGD  hyperparameters and thus is a candidate for wide deployment.
answer:
Accelerating SGD by arranging examples differently


question:
Deep learning approaches usually require a large amount of labeled data to generalize. However, humans can learn a new concept only by a few samples. One of the high cogntition human capablities is to learn several concepts at the same time. In this paper, we address the task of classifying multiple objects by seeing only a few samples from each category. To the best of authors' knowledge, there is no dataset specially designed for few-shot multiclass classification. We design a task of mutli-object few class classification and an environment for easy creating controllable datasets for this task. We demonstrate that the proposed dataset is sound using a method which is an extension of prototypical networks.
answer:

We introduce a diagnostic task which is a variation of few-shot learning and introduce a dataset for it.