论文标题
深纳格：深度非对抗性的手势产生
DeepNAG: Deep Non-Adversarial Gesture Generation
论文作者
论文摘要
合成数据生成以提高分类性能（数据增强）是一个充分研究的问题。最近，生成的对抗网络（GAN）显示出了出色的图像数据增强性能，但是它们在手势合成中的适用性已受到关注不足。此外，甘斯（Gans）非常需要同时进行发电机和歧视者网络培训。我们在这项工作中解决了这两个问题。我们首先讨论了一种新型的，device-nostic的gan模型，用于称为deepgan的手势合成。此后，我们通过基于动态时间扭曲和平均Hausdorff距离引入新的可区分损耗函数来制定深色，这使我们能够训练Deepgan的生成器而无需歧视器。通过评估，我们将DeepGan和DeepNag的实用性与两种替代技术使用六个数据集的数据增强培训五个识别者进行了比较。我们通过基于炒作基准的亚马逊机械Turk用户研究进一步研究了合成样品的感知质量。我们发现，DeepNag在准确性，训练时间（最高17倍）和现实主义方面优于Deepgan，从而为发电机网络设计和手势合成中的新研究开辟了大门。我们的源代码可在https://www.deepnag.com上找到。
Synthetic data generation to improve classification performance (data augmentation) is a well-studied problem. Recently, generative adversarial networks (GAN) have shown superior image data augmentation performance, but their suitability in gesture synthesis has received inadequate attention. Further, GANs prohibitively require simultaneous generator and discriminator network training. We tackle both issues in this work. We first discuss a novel, device-agnostic GAN model for gesture synthesis called DeepGAN. Thereafter, we formulate DeepNAG by introducing a new differentiable loss function based on dynamic time warping and the average Hausdorff distance, which allows us to train DeepGAN's generator without requiring a discriminator. Through evaluations, we compare the utility of DeepGAN and DeepNAG against two alternative techniques for training five recognizers using data augmentation over six datasets. We further investigate the perceived quality of synthesized samples via an Amazon Mechanical Turk user study based on the HYPE benchmark. We find that DeepNAG outperforms DeepGAN in accuracy, training time (up to 17x faster), and realism, thereby opening the door to a new line of research in generator network design and training for gesture synthesis. Our source code is available at https://www.deepnag.com.
