Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language descriptions. In this paper, we propose a novel generative adversarial network, RankGAN, for generating high-quality language descriptions. Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collection of human-written and machine-written sentences by giving a reference group. By viewing a set of data samples collectively and evaluating their quality through relative ranking scores, the discriminator is able to make better assessment which in turn helps to learn a better generator. The proposed RankGAN is optimized through the policy gradient technique. Experimental results on multiple public datasets clearly demonstrate the effectiveness of the proposed approach.

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We perform a wide variety of experiments and demonstrate that OST outperforms the existing algorithms in the low-shot scenario. On most datasets the method also presents a comparable accuracy with a single training example to the accuracy obtained by the other methods for the entire set of domainA images.

This,inturn,points out sufficient regularity conditions that maps must obey to carry out successful translations.

Despite its potential benefits, applying DST to GANs presents challenges due to the adversarial nature of the training process.

Somemodelcompression methods have been successfully applied to image classification and detection or language models, but there has been very little work compressing generative adversarial networks(GANs) performing complextasks.

The continued scaling of flash memory technology into smaller process nodes, combined with the increased information capacity of each flash cell (i.e, storing more bits per cell), has placed NAND flash memory at the forefront of modern storage technology.

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Furthermore, we show that the WAE objective is related to other statistical quantities such as thef-divergence and in particular, upper bounded by the Wasserstein distance, which then allows us to tap into existing efficient(regularized)optimaltransportsolvers.