The garment transfer problem comprises two tasks: learning to separate a
person's body (pose, shape, color) from their clothing (garment type, shape,
style) and then generating new images of the wearer dressed in arbitrary
garments. We present GarmentGAN, a new algorithm that performs image-based
garment transfer through generative adversarial methods. The GarmentGAN
framework allows users to virtually try-on items before purchase and
generalizes to various apparel types. GarmentGAN requires as input only two
images, namely, a picture of the target fashion item and an image containing
the customer. The output is a synthetic image wherein the customer is wearing
the target apparel. In order to make the generated image look photo-realistic,
we employ the use of novel generative adversarial techniques. GarmentGAN
improves on existing methods in the realism of generated imagery and solves
various problems related to self-occlusions. Our proposed model incorporates
additional information during training, utilizing both segmentation maps and
body key-point information. We show qualitative and quantitative comparisons to
several other networks to demonstrate the effectiveness of this technique.

GarmentGAN 是一种使用生成对抗方法进行图像化服装转移的算法，可以根据用户提供的两张图像，合成新的虚拟试衣图片，通过在训练中加入分割地图和人体关键点信息，提升了图像的真实性和效果。

GarmentGAN：照片逼真对抗时尚转换

GarmentGAN: Photo-realistic Adversarial Fashion Transfer

We consider the problem of imitation learning from a finite set of expert
trajectories, without access to reinforcement signals. The classical approach
of extracting the expert's reward function via inverse reinforcement learning,
followed by reinforcement learning is indirect and may be computationally
expensive. Recent generative adversarial methods based on matching the policy
distribution between the expert and the agent could be unstable during
training. We propose a new framework for imitation learning by estimating the
support of the expert policy to compute a fixed reward function, which allows
us to re-frame imitation learning within the standard reinforcement learning
setting. We demonstrate the efficacy of our reward function on both discrete
and continuous domains, achieving comparable or better performance than the
state of the art under different reinforcement learning algorithms.

本文提出一种新的模仿学习框架，通过估计专家策略的支持来计算固定的奖励函数，将模仿学习重新定位到标准的强化学习设置中，证明了该奖励函数在离散和连续域上的有效性，并在不同的强化学习算法下实现了与现有技术相当或更好的表现。

随机专家蒸馏：通过专家策略支持来进行模仿学习估计

Random Expert Distillation: Imitation Learning via Expert Policy Support  Estimation

This paper first presents a theory for generative adversarial methods that
does not rely on the traditional minimax formulation. It shows that with a
strong discriminator, a good generator can be learned so that the KL divergence
between the distributions of real data and generated data improves after each
functional gradient step until it converges to zero. Based on the theory, we
propose a new stable generative adversarial method. A theoretical insight into
the original GAN from this new viewpoint is also provided. The experiments on
image generation show the effectiveness of our new method.

本文提出了一种不依赖于传统极小 - 极大公式的生成式对抗方法理论，并展示了当存在强的辨别器时，通过每个功能性梯度步骤，可以学到一个好的生成器，使得真实数据和生成数据的分布的 KL 散度改善，直到收敛于零，并基于该理论，提出了一种新的稳定的生成式对抗方法，同时提供了从这个新的视角对原始 GAN 的理论洞见，最终，针对图像生成的实验展示了我们的新方法的有效性。