论文标题
对抗性网络训练使用改良的瓦斯坦距离中的高阶矩训练
Adversarial network training using higher-order moments in a modified Wasserstein distance
论文作者
论文摘要
生成 - 对抗性网络（GAN）已被用来在压缩的潜在空间中与示例数据紧密相似，该数据与原始矢量空间中的重建足够接近。 Wasserstein度量已被用作二进制跨凝结的替代方法，产生了具有更大模式覆盖行为的数字稳定gan。在这里，使用比平均值的高阶矩对Wasserstein距离的概括。用这种高阶Wasserstein度量训练gan也可以表现出较高的性能，即使调整了较高的计算成本。这是生成合成抗体序列的说明。
Generative-adversarial networks (GANs) have been used to produce data closely resembling example data in a compressed, latent space that is close to sufficient for reconstruction in the original vector space. The Wasserstein metric has been used as an alternative to binary cross-entropy, producing more numerically stable GANs with greater mode covering behavior. Here, a generalization of the Wasserstein distance, using higher-order moments than the mean, is derived. Training a GAN with this higher-order Wasserstein metric is demonstrated to exhibit superior performance, even when adjusted for slightly higher computational cost. This is illustrated generating synthetic antibody sequences.
