Ensuring the realism of computer-generated synthetic images is crucial to
deep neural network (DNN) training. Due to different semantic distributions
between synthetic and real-world captured datasets, there exists semantic
mismatch between synthetic and refined images, which in turn results in the
semantic distortion. Recently, contrastive learning (CL) has been successfully
used to pull correlated patches together and push uncorrelated ones apart. In
this work, we exploit semantic and structural consistency between synthetic and
refined images and adopt CL to reduce the semantic distortion. Besides, we
incorporate hard negative mining to improve the performance furthermore. We
compare the performance of our method with several other benchmarking methods
using qualitative and quantitative measures and show that our method offers the
state-of-the-art performance.

该论文研究了深度神经网络模型训练中的计算机生成图像的真实性问题，使用对比度学习和硬负样本挖掘方法，实现了对计算机生成和真实图像之间的语义和结构一致性匹配，从而得到了最先进的性能表现。

对比学习和一致性语义和结构约束的无监督合成图像细化

Unsupervised Synthetic Image Refinement via Contrastive Learning and  Consistent Semantic and Structure Constraints

Although Generative Adversarial Network (GAN) can be used to generate the
realistic image, improper use of these technologies brings hidden concerns. For
example, GAN can be used to generate a tampered video for specific people and
inappropriate events, creating images that are detrimental to a particular
person, and may even affect that personal safety. In this paper, we will
develop a deep forgery discriminator (DeepFD) to efficiently and effectively
detect the computer-generated images. Directly learning a binary classifier is
relatively tricky since it is hard to find the common discriminative features
for judging the fake images generated from different GANs. To address this
shortcoming, we adopt contrastive loss in seeking the typical features of the
synthesized images generated by different GANs and follow by concatenating a
classifier to detect such computer-generated images. Experimental results
demonstrate that the proposed DeepFD successfully detected 94.7% fake images
generated by several state-of-the-art GANs.

本研究提出了一个名为 DeepFD 的深度伪造鉴别器，采用对比损失的方法来检测由不同 GAN 生成的计算机生成图像，实验结果表明，DeepFD 可以有效地检测到几种最新的 GAN 生成的 94.7% 假图像。