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.
Feature-aware conditional GAN for category text generation
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Interdisciplinary Graduate School, Nanyang Technological University（南洋理工大学跨学科研究生院）
Conditional COT-GAN for Video Prediction with Kernel Smoothing
London School of Economics（伦敦政治经济学院）；ETH Zurich（苏黎世联邦理工学院）
因果最优运输（COT）是在经典的最优运输问题上施加时间因果约束的结果，它自然地产生了一个新的概念，即路径空间上分布之间的距离。Xu等人（2020）首次将COT理论应用于顺序学习，其中COT-GAN被引入作为对抗算法，用于训练优化的隐式生成模型以产生顺序数据。依靠（Xu et al.，2020年），本文的贡献是双重的。首先，我们开发了一个适合序列预测的COT-GAN的条件版本。这意味着现在使用数据集来了解序列在观察其过去演变的情况下将如何演变。其次，我们通过核平滑修改经验测量来改进收敛结果（Pflug and Pichler（2016））。所得到的内核条件COT-GAN算法与视频预测的应用程序进行说明。
RCC-GAN: Regularized Compound Conditional GAN for Large-Scale Tabular Data Synthesis
École de Technologie Supérieure (ÉTS), Université du Québec（魁北克大学高等技术学院）；Fédération des Caisses Desjardins du Québec（魁北克德信联合会）；University of Windsor（温莎大学）
Interpreting CNN Predictions using Conditional Generative Adversarial Networks
Amrita Vishwa Vidyapeetham（阿米塔大学）；Universitat Politècnica de Catalunya (UPC-BarcelonaTECH)（加泰罗尼亚理工大学（巴塞罗那理工大学））；Institut de Recerca Sant Joan de Déu (IRSJD)（圣胡安·德·德乌研究所）
Expression Conditional GAN for Facial Expression-to-Expression Translation
University of Trento（特伦托大学）；EPFL（洛桑联邦理工学院）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Huazhong University of Science and Technology（华中科技大学）；University of Oxford（牛津大学）；Texas State University（德克萨斯州立大学）
Conditional LSTM-GAN for Melody Generation from Lyrics
National Institute of Informatics（国立情报学研究所）；Indraprastha Institute of Information Technology Delhi（德里印度信息技术学院）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
