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Unsupervised Image-to-Image Translation Networks
[article]
2018
arXiv
pre-print
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To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs. ...
Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. ...
We plan to address these issues in the future work.
A Network Architecture The network architecture used for the unsupervised image-to-image translation experiments is given in Table 3 . ...
arXiv:1703.00848v6
fatcat:e5ixr7aeszdi5avxout5rtsibm
Unsupervised Image-to-Image Translation with Self-Attention Networks
[article]
2019
arXiv
pre-print
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However, the effectiveness of the self-attention network in unsupervised image-to-image translation tasks have not been verified. ...
In this paper, we propose an unsupervised image-to-image translation with self-attention networks, in which long range dependency helps to not only capture strong geometric change but also generate details ...
Methods
Unpaired Image-to-Image Translation with Self Attention Networks We propose an unsupervised image-to-image translation model with self-attention networks that allows long range dependency modeling ...
arXiv:1901.08242v3
fatcat:2fw2oclkyzaobjzstr2xd5s5ke
Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 Network
[article]
2020
arXiv
pre-print
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Image-to-Image (I2I) translation is a heated topic in academia, and it also has been applied in real-world industry for tasks like image synthesis, super-resolution, and colorization. ...
and reference images compared to state-of-the-art works. ...
CycleGAN is the first unsupervised I2I translation network that proposed cycle consistency loss to ensure identity invariance between the input and output images. ...
arXiv:2010.05713v2
fatcat:clr3s36ivjazpnatlko3igj4fi
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
[article]
2019
arXiv
pre-print
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The state-of-the-art approaches in Generative Adversarial Networks (GANs) are able to learn a mapping function from one image domain to another with unpaired image data. ...
The attention-guided generators in AGGAN are able to produce attention masks via a built-in attention mechanism, and then fuse the input image with the attention mask to obtain a target image with high-quality ...
The contributions of this paper are summarized as follows: • We propose a novel Attention-Guided Generative Adversarial Network (AGGAN) for unsupervised image-to-image translation. • We propose a novel ...
arXiv:1903.12296v3
fatcat:xcsgwhsf6nfmppgjvrjdg2fwga
Unsupervised Image-to-Image Translation with Generative Adversarial Networks
[article]
2017
arXiv
pre-print
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We define this requirement as the "image-to-image translation" problem, and propose a general approach to achieve it, based on deep convolutional and conditional generative adversarial networks (GANs), ...
In this work, we develop a two step (unsupervised) learning method to translate images between different domains by using unlabeled images without specifying any correspondence between them, so that to ...
Recently, another work [20] demonstrated an unsupervised approach for image-to-image translation, the training images do not need to have a specific input and target. ...
arXiv:1701.02676v1
fatcat:cy2l42j6pfhxfgefumbooc3ure
Unsupervised Image to Sequence Translation with Canvas-Drawer Networks
[article]
2018
arXiv
pre-print
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desired image recreation or translation task. ...
Specifically, we train a "canvas" network to imitate the mapping of high-level constructs to pixels, followed by a high-level "drawing" network which is optimized through this mapping towards solving a ...
CAN CANVAS-DRAWER PAIRS BE USED TO VECTORIZE SYMBOLS IN AN UNSUPERVISED MANNER? ...
arXiv:1809.08340v2
fatcat:rfhr2n5uhvfrres2l7k7oxswhe
Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks
[article]
2018
arXiv
pre-print
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Recent studies on unsupervised image-to-image translation have made a remarkable progress by training a pair of generative adversarial networks with a cycle-consistent loss. ...
but also enable higher resolution image-to-image translations in a coarse-to-fine manner. ...
Different form the existing works, this work exploits stacked image-to-image translation networks coupled with a novel adaptive fusion block to tackle the unsupervised image-to-image translation problem ...
arXiv:1807.08536v2
fatcat:lt3kdoyvsng7jdp36cjj7c3plq
U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
[article]
2020
arXiv
pre-print
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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-to-end manner. ...
Unlike previous attention-based method which cannot handle the geometric changes between domains, our model can translate both images requiring holistic changes and images requiring large shape changes ...
UNSUPERVISED GENERATIVE ATTENTIONAL NETWORKS WITH ADAPTIVE LAYER-INSTANCE NORMALIZATION Our goal is to train a function G s→t that maps images from a source domain X s to a target domain X t using only ...
arXiv:1907.10830v4
fatcat:u4bey3rm4zh27cyl6zmljgrfwu
In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks
[article]
2017
arXiv
pre-print
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In this paper, we propose an extension of the unsupervised image-to-image translation problem to multiple input setting. ...
In unsupervised image-to-image translation, the goal is to learn the mapping between an input image and an output image using a set of unpaired training images. ...
Introduction The problem of unsupervised image-to-image translation has made promising strides with the advent of Generative Adversarial Networks (GAN) [5] in recent years. ...
arXiv:1711.09334v1
fatcat:snknh767cnfvblemzpye5e337u
Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks
[chapter]
2018
Lecture Notes in Computer Science
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Recent studies on unsupervised image-to-image translation have made remarkable progress by training a pair of generative adversarial networks with a cycle-consistent loss. ...
but also enable higher resolution image-toimage translation in a coarse-to-ine fashion. ...
To the best of our knowledge, there exists no attempt to exploit stacked networks to overcome the diiculties of learning unsupervised image-to-image translation. ...
doi:10.1007/978-3-030-01240-3_12
fatcat:mxwsmymx45hgzc7sqrbvxnw6yy
Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation
[article]
2020
arXiv
pre-print
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State-of-the-art techniques in Generative Adversarial Networks (GANs) have shown remarkable success in image-to-image translation from peer domain X to domain Y using paired image data. ...
In this work, we propose a general framework for unsupervised image-to-image translation across multiple domains, which can translate images from domain X to any a domain without requiring direct training ...
CROSS-DOMAIN GENERATIVE ADVERSARIAL NETWORK To conduct unsupervised multi-domain image-to-image translation, a direct approach is to train a CycleGAN for every two domains. ...
arXiv:2008.11882v1
fatcat:jl3ztv5herhotcdlxs46h6ttn4
DuCaGAN: Unified Dual Capsule Generative Adversarial Network for Unsupervised Image-to-Image Translation
2020
IEEE Access
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Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2020.3007266, IEEE Access ...
(iii) The capsule network is introduced as the main discriminator of multi-agent competition mechanism for the unsupervised image-to-image translation. ...
In this work, we fully utilized the superior competition mechanism of multi-agent [24] and the capsule network [25] in the GAN model to solve the unsupervised image-to-image translation problem. ...
doi:10.1109/access.2020.3007266
fatcat:7dik7bf5e5aodb76swkb5nswgy
Unsupervised Medical Image Translation Using Cycle-MedGAN
[article]
2019
arXiv
pre-print
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Image-to-image translation is a new field in computer vision with multiple potential applications in the medical domain. ...
On the other hand, unsupervised translation frameworks often result in blurred translated images with unrealistic details. ...
Several methods for unsupervised image-to-image translation have been developed. ...
arXiv:1903.03374v1
fatcat:6eittos3tjhbrddjdi4optojqu
Unsupervised object detection via LWIR/RGB translation
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Object-specific CycleGAN unsupervised adaption In this section we first describe the cycleGAN network and then our novel unsupervised adaptation to enhance the object quality in adapted images. ...
Image translation networks For the last few years, researchers have looked at generative adversarial networks (GANs) to adapt source imagery to a different target domain [6, 9, 19, 11, 16, 7, 17] . ...
doi:10.1109/cvprw50498.2020.00053
dblp:conf/cvpr/AbbottRRC20
fatcat:yor67micorfsxoya3ny7mpp4qq
Unsupervised Image Super-Resolution with an Indirect Supervised Path
[article]
2019
arXiv
pre-print
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There are several attempts that directly apply unsupervised image translation models to address such a problem. ...
real LR images to HR images. ...
Unsupervised image translation. Image super-resolution can be considered as a special image translation task, i.e., translating images from LR domain to HR domain. ...
arXiv:1910.02593v2
fatcat:fzdh5sjk2nbkpc7fdgcpkvrpoq
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