论文标题
关于接受场在无监督的SIM到真实图像翻译中的作用
On the Role of Receptive Field in Unsupervised Sim-to-Real Image Translation
论文作者
论文摘要
生成对抗网络（GAN）现在被广泛用于光真逼真的图像合成。在需要将模拟图像转换为现实映像（SIM到真实）的应用程序中，经过对两个域的未配对数据训练的gan易于在语义内容保留中失败，因为图像从一个域转换为另一个域。在实际数据缺乏内容多样性的情况下，这种故障模式更为明显，从而导致两个域之间的内容\ emph {不匹配} - 在现实世界部署中经常遇到的情况。在本文中，我们调查了歧视者接受场在gan中的作用，对于不匹配的数据，无监督的图像到图像翻译，并研究了其对语义含量保留的影响。对最先进的耦合耦合自动编码器（VAE）的歧视架构的实验 - 不同的数据集对gan模型表明，歧视器接受场与所产生图像的语义内容差异直接相关。
Generative Adversarial Networks (GANs) are now widely used for photo-realistic image synthesis. In applications where a simulated image needs to be translated into a realistic image (sim-to-real), GANs trained on unpaired data from the two domains are susceptible to failure in semantic content retention as the image is translated from one domain to the other. This failure mode is more pronounced in cases where the real data lacks content diversity, resulting in a content \emph{mismatch} between the two domains - a situation often encountered in real-world deployment. In this paper, we investigate the role of the discriminator's receptive field in GANs for unsupervised image-to-image translation with mismatched data, and study its effect on semantic content retention. Experiments with the discriminator architecture of a state-of-the-art coupled Variational Auto-Encoder (VAE) - GAN model on diverse, mismatched datasets show that the discriminator receptive field is directly correlated with semantic content discrepancy of the generated image.
