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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 …
Few-shot unsupervised image-to-image translation
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 …
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
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 …
incorporates a new attention module and a new learnable normalization function in an end …
A survey of unsupervised deep domain adaptation
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 …
approaches for supervised learning have performed well, they assume that training and …
Multimodal unsupervised image-to-image translation
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 …
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
Recently, contrastive learning-based image translation methods have been proposed, which
contrasts different spatial locations to enhance the spatial correspondence. However, the …
contrasts different spatial locations to enhance the spatial correspondence. However, the …