A review on generative adversarial networks: Algorithms, theory, and applications
Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …
however, they have been studied since 2014, and a large number of algorithms have been …
Image steganography: A review of the recent advances
Image Steganography is the process of hiding information which can be text, image or video
inside a cover image. The secret information is hidden in a way that it not visible to the …
inside a cover image. The secret information is hidden in a way that it not visible to the …
[HTML][HTML] Deep learning-based transformation of H&E stained tissues into special stains
Pathology is practiced by visual inspection of histochemically stained tissue slides. While the
hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide …
hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide …
CT super-resolution GAN constrained by the identical, residual, and cycle learning ensemble (GAN-CIRCLE)
In this paper, we present a semi-supervised deep learning approach to accurately recover
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …
high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the …
Augmented cyclegan: Learning many-to-many mappings from unpaired data
Learning inter-domain mappings from unpaired data can improve performance in structured
prediction tasks, such as image segmentation, by reducing the need for paired data …
prediction tasks, such as image segmentation, by reducing the need for paired data …
Variational mixture-of-experts autoencoders for multi-modal deep generative models
Learning generative models that span multiple data modalities, such as vision and
language, is often motivated by the desire to learn more useful, generalisable …
language, is often motivated by the desire to learn more useful, generalisable …
Learning to generate line drawings that convey geometry and semantics
This paper presents an unpaired method for creating line drawings from photographs.
Current methods often rely on high quality paired datasets to generate line drawings …
Current methods often rely on high quality paired datasets to generate line drawings …
Distribution matching losses can hallucinate features in medical image translation
This paper discusses how distribution matching losses, such as those used in CycleGAN,
when used to synthesize medical images can lead to mis-diagnosis of medical conditions. It …
when used to synthesize medical images can lead to mis-diagnosis of medical conditions. It …
Ntire 2020 challenge on real-world image super-resolution: Methods and results
A Lugmayr, M Danelljan… - Proceedings of the IEEE …, 2020 - openaccess.thecvf.com
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on
the participating methods and final results. The challenge addresses the real world setting …
the participating methods and final results. The challenge addresses the real world setting …
Rl-cyclegan: Reinforcement learning aware simulation-to-real
Deep neural network based reinforcement learning (RL) can learn appropriate visual
representations for complex tasks like vision-based robotic grasping without the need for …
representations for complex tasks like vision-based robotic grasping without the need for …