We present HandGAN (H-GAN), a cycle-consistent adversarial learning approach
implementing multi-scale perceptual discriminators. It is designed to translate
synthetic images of hands to the real domain. Synthetic hands provide complete
ground-truth annotations, yet they are not representative of the target
distribution of real-world data. We strive to provide the perfect blend of a
realistic hand appearance with synthetic annotations. Relying on image-to-image
translation, we improve the appearance of synthetic hands to approximate the
statistical distribution underlying a collection of real images of hands. H-GAN
tackles not only the cross-domain tone mapping but also structural differences
in localized areas such as shading discontinuities. Results are evaluated on a
qualitative and quantitative basis improving previous works. Furthermore, we
relied on the hand classification task to claim our generated hands are
statistically similar to the real domain of hands.

本论文提出 HandGAN，一种采用多尺度感知判别器的循环一致性对抗性学习方法，旨在将合成的手部图像转换为真实领域中的图像，从而提高人工合成手部图像的逼真程度。

H-GAN: 手中的 GAN 力量

H-GAN: the power of GANs in your Hands

We formalize the problem of learning interdomain correspondences in the
absence of paired data as Bayesian inference in a latent variable model (LVM),
where one seeks the underlying hidden representations of entities from one
domain as entities from the other domain. First, we introduce implicit latent
variable models, where the prior over hidden representations can be specified
flexibly as an implicit distribution. Next, we develop a new variational
inference (VI) algorithm for this model based on minimization of the symmetric
Kullback-Leibler (KL) divergence between a variational joint and the exact
joint distribution. Lastly, we demonstrate that the state-of-the-art
cycle-consistent adversarial learning (CYCLEGAN) models can be derived as a
special case within our proposed VI framework, thus establishing its connection
to approximate Bayesian inference methods.

本文利用拉丁变量模型和贝叶斯推理方法，提出了一种隐式拉丁变量模型的变分推理算法，用于解决缺少成对数据时的领域间对应关系的学习问题，并证明了该算法是近似贝叶斯推理方法的 CYCLEGAN 模型的特例。