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 a deep generative model, named Monge-Amp\`ere flow, which builds on continuous-time gradient flow arising from the Monge-Amp\`ere equation in optimal transport theory. The generative map from the latent space to the data space follows a dynamical system, where a learnable potential function guides a compressible fluid to flow towards the target density distribution. Training of the model amounts to solving an optimal control problem. The Monge-Amp\`ere flow has tractable likelihoods and supports efficient sampling and inference. One can easily impose symmetry constraints in the generative model by designing suitable scalar potential functions. We apply the approach to unsupervised density estimation of the MNIST dataset and variational calculation of the two-dimensional Ising model at the critical point. This approach brings insights and techniques from Monge-Amp\`ere equation, optimal transport, and fluid dynamics into reversible flow-based generative models.
answer:
A gradient flow based dynamical system for invertible generative modeling


question:
For natural language understanding (NLU) technology to be maximally useful, it must be able to process language in a way that is not exclusive to a single task, genre, or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation (GLUE) benchmark, a collection of tools for evaluating the performance of models across a diverse set of existing NLU tasks. By including tasks with limited training data, GLUE is designed to favor and encourage models that share general linguistic knowledge across tasks. GLUE also includes a hand-crafted diagnostic test suite that enables detailed linguistic analysis of models. We evaluate baselines based on current methods for transfer and representation learning and find that multi-task training on all tasks performs better than training a separate model per task. However, the low absolute performance of our best model indicates the need for improved general NLU systems.
answer:
We present a multi-task benchmark and analysis platform for evaluating generalization in natural language understanding systems.


question:
The rich and accessible labeled data fuel the revolutionary success of deep learning. Nonetheless, massive supervision remains a luxury for many real applications, boosting great interest in label-scarce techniques such as few-shot learning (FSL). An intuitively feasible approach to FSL is to conduct data augmentation via synthesizing additional training samples. The key to this approach is how to guarantee both discriminability and diversity of the synthesized samples. In this paper, we propose a novel FSL model, called $\textrm{D}^2$GAN, which synthesizes Diverse and Discriminative features based on Generative Adversarial Networks (GAN). $\textrm{D}^2$GAN secures discriminability of the synthesized features by constraining them to have high correlation with real features of the same classes while low correlation with those of different classes.   Based on the observation that noise vectors that are closer in the latent code space are more likely to be collapsed into the same mode when mapped to feature space, $\textrm{D}^2$GAN incorporates a novel anti-collapse regularization term, which encourages feature diversity by penalizing the ratio of the logarithmic similarity of two synthesized features and the logarithmic similarity of the latent codes generating them. Experiments on three common benchmark datasets verify the effectiveness of $\textrm{D}^2$GAN by comparing with the state-of-the-art.
answer:

A new GAN based few-shot learning algorithm by synthesizing  diverse and discriminative Features