Although existing image-to-image translation methods can map an image from the source domain to the target domain, the translation results are prone to visual artifacts, and the texture and shape of the input image cannot match the target domain well. The reason for this phenomenon is that ...
Conditional Generative Adversarial Networks (GANs) for cross-domain image-to-image translation have made much progress recently. Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a conditional GAN. However, human labeling is expensive, even impractical,...
Unsupervised domain adaptation (UDA) has recently garnered widespread attention in the field of medical image segmentation by transferring knowledge from labeled source datasets to enhance model segmentation performance on unlabeled target domain data. A
Recent studies have shown remarkable success in unsupervised image-to-image translation. However, if there has no access to enough images in target classes, learning a mapping from source classes to the target classes always suffers from mode collapse, which limits the application of the existing ...
- 《Applied Soft Computing》 - 2024 - 被引量: 0 BioGAN: An unpaired GAN-based image to image translation model for microbiological images Saber Mirzaee Bafti,C. Ang,G. Marcelli,... - 《Arxiv》 - 2023 - 被引量: ...
The FID and KID are used to evaluate the visual quality of generated images, and both measure the distribution divergence between the generated images and the real images57. These metrics are the most well-accepted for measuring the images generated by unsupervised image translation models. For eac...
UpdatedJun 18, 2024 C++ Load more… Improve this page Add a description, image, and links to theunsupervisedtopic page so that developers can more easily learn about it. To associate your repository with theunsupervisedtopic, visit your repo's landing page and select "manage topics."...
The generator performs image-to-image translation from low dose to high dose. The discriminators are PatchGAN networks that return the patch-wise probability that the input data is real or generated. One discriminator distinguishes between the real and generated low-dose images and the other ...
Network structures of the generator and the discriminator used in image-to-image translation 图像翻译中采用的生成器与判别器网络结构 7 minλcadvLcadv+λdadvLdadv+λcycleLcycle+λselfLself+λKLLKLminλadvcLadvc+λadvdLadvd+λcycleLcycle+λselfLself+λKLLKL 式中, λcadv、λdadv、λcycle、...
The development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation, which is gradually changing the landscape of optical imaging and biomedical researc