Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review
Purpose In radiotherapy, MRI is used for target volume and organs-at-risk delineation for its
superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the …
superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the …
Recent advances in deep learning: An overview
Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence
research. It is also one of the most popular scientific research trends now-a-days. Deep …
research. It is also one of the most popular scientific research trends now-a-days. Deep …
A u-net based discriminator for generative adversarial networks
E Schonfeld, B Schiele… - Proceedings of the IEEE …, 2020 - openaccess.thecvf.com
Among the major remaining challenges for generative adversarial networks (GANs) is the
capacity to synthesize globally and locally coherent images with object shapes and textures …
capacity to synthesize globally and locally coherent images with object shapes and textures …
Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks
We consider the single image super-resolution problem in a more general case that the low-
/high-resolution pairs and the down-sampling process are unavailable. Different from …
/high-resolution pairs and the down-sampling process are unavailable. Different from …
Image to image translation for domain adaptation
Z Murez, S Kolouri, D Kriegman… - Proceedings of the …, 2018 - openaccess.thecvf.com
We propose a general framework for unsupervised domain adaptation, which allows deep
neural networks trained on a source domain to be tested on a different target domain without …
neural networks trained on a source domain to be tested on a different target domain without …
Adversarial learning for semi-supervised semantic segmentation
We propose a method for semi-supervised semantic segmentation using an adversarial
network. While most existing discriminators are trained to classify input images as real or …
network. While most existing discriminators are trained to classify input images as real or …
Multiple cycle-in-cycle generative adversarial networks for unsupervised image super-resolution
With the help of convolutional neural networks (CNN), the single image super-resolution
problem has been widely studied. Most of these CNN based methods focus on learning a …
problem has been widely studied. Most of these CNN based methods focus on learning a …
A generative adversarial approach for zero-shot learning from noisy texts
Most existing zero-shot learning methods consider the problem as a visual semantic
embedding one. Given the demonstrated capability of Generative Adversarial Networks …
embedding one. Given the demonstrated capability of Generative Adversarial Networks …
[HTML][HTML] Countering malicious deepfakes: Survey, battleground, and horizon
The creation or manipulation of facial appearance through deep generative approaches,
known as DeepFake, have achieved significant progress and promoted a wide range of …
known as DeepFake, have achieved significant progress and promoted a wide range of …
Msg-gan: Multi-scale gradients for generative adversarial networks
A Karnewar, O Wang - … of the IEEE/CVF conference on …, 2020 - openaccess.thecvf.com
Abstract While Generative Adversarial Networks (GANs) have seen huge successes in
image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due …
image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due …