A review on generative adversarial networks: Algorithms, theory, and applications

J Gui, Z Sun, Y Wen, D Tao, J Ye - IEEE transactions on …, 2021‏ - ieeexplore.ieee.org
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 …

Few-shot unsupervised image-to-image translation

MY Liu, X Huang, A Mallya, T Karras… - Proceedings of the …, 2019‏ - openaccess.thecvf.com
Unsupervised image-to-image translation methods learn to map images in a given class to
an analogous image in a different class, drawing on unstructured (non-registered) datasets …

U-gat-it: Unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation

J Kim, M Kim, H Kang, K Lee - arxiv preprint arxiv:1907.10830, 2019‏ - arxiv.org
We propose a novel method for unsupervised image-to-image translation, which
incorporates a new attention module and a new learnable normalization function in an end …

A survey of unsupervised deep domain adaptation

G Wilson, DJ Cook - ACM Transactions on Intelligent Systems and …, 2020‏ - dl.acm.org
Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

Multimodal unsupervised image-to-image translation

X Huang, MY Liu, S Belongie… - Proceedings of the …, 2018‏ - openaccess.thecvf.com
Unsupervised image-to-image translation is an important and challenging problem in
computer vision. Given an image in the source domain, the goal is to learn the conditional …

Exploring patch-wise semantic relation for contrastive learning in image-to-image translation tasks

C Jung, G Kwon, JC Ye - … of the IEEE/CVF conference on …, 2022‏ - openaccess.thecvf.com
Recently, contrastive learning-based image translation methods have been proposed, which
contrasts different spatial locations to enhance the spatial correspondence. However, the …