论文标题
有条件的杂种gan用于序列产生
Conditional Hybrid GAN for Sequence Generation
论文作者
论文摘要
有条件的序列生成旨在通过使用其他上下文信息来调节模型，这是一种自我监督的学习问题（一种无监督的学习形式，并从数据本身中进行监督信息）。不幸的是，当前的最新生成模型具有多个属性的序列生成的局限性。在本文中，我们提出了一种新型的条件杂种gan（C-Hybrid-gan）来解决此问题。在相同上下文中，分别生成具有三重态属性的离散序列。最重要的是，利用关系推理技术不仅模拟了生成器训练期间属性的每个序列内部的依赖性，而且还建模属性序列之间的一致性。 To avoid the non-differentiability problem in GANs encountered during discrete data generation, we exploit the Gumbel-Softmax technique to approximate the distribution of discrete-valued sequences.Through evaluating the task of generating melody (associated with note, duration, and rest) from lyrics, we demonstrate that the proposed C-Hybrid-GAN outperforms the existing methods in context-conditioned discrete-valued sequence generation.
Conditional sequence generation aims to instruct the generation procedure by conditioning the model with additional context information, which is a self-supervised learning issue (a form of unsupervised learning with supervision information from data itself). Unfortunately, the current state-of-the-art generative models have limitations in sequence generation with multiple attributes. In this paper, we propose a novel conditional hybrid GAN (C-Hybrid-GAN) to solve this issue. Discrete sequence with triplet attributes are separately generated when conditioned on the same context. Most importantly, relational reasoning technique is exploited to model not only the dependency inside each sequence of the attribute during the training of the generator but also the consistency among the sequences of attributes during the training of the discriminator. To avoid the non-differentiability problem in GANs encountered during discrete data generation, we exploit the Gumbel-Softmax technique to approximate the distribution of discrete-valued sequences.Through evaluating the task of generating melody (associated with note, duration, and rest) from lyrics, we demonstrate that the proposed C-Hybrid-GAN outperforms the existing methods in context-conditioned discrete-valued sequence generation.
