A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Domain-Specific Mappings for Generative Adversarial Style Transfer
[article]
2020
arXiv
pre-print
Preserved Fulltext
For addressing this issue, this paper leverages domain-specific mappings for remapping latent features in the shared content space to domain-specific content spaces. ...
Style transfer generates an image whose content comes from one image and style from the other. ...
This work was supported in part by MOST under grant 107-2221-E-002-147-MY3 and MOST Joint Research Center for AI Technology and All Vista Healthcare under grant 109-2634-F-002-032. ...
arXiv:2008.02198v1
fatcat:tqofuqhpr5eqpgdr3s57hzj7ue
Image to Image Translation using Deep Learning Techniques
2020
International Journal of Computer Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We have generated images for different tasks. Our innovation is we train and test the cycle-consistent adversarial networks using dataset. ...
The experiment outcomes show that our method can successfully transfer for disparate tasks while conserving the original content. ...
For neural style transfer task, we perform 200 iterations for a given content and style image, and used a pre-trained VGG model to generate the artistic image. ...
doi:10.5120/ijca2020920745
fatcat:kilrichqqzetfeo2vxaemijddm
Multi-Style Unsupervised image synthesis us-ing Generative Adversarial Nets
2021
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Firstly, the encoder-decoder structure is used to map the image to domain-shared content features space and domain-specific style features space. ...
A Multi-Style Unsupervised Feature-Wise image synthesis model using Generative Adversarial Nets (MSU-FW-GAN) based on the MSU-GAN is proposed for the shape variation tasks. ...
For example, in domain , the content encoder maps images onto domain-shared content space , and style encoders maps images onto domainspecific style space . ...
doi:10.1109/access.2021.3087665
fatcat:6qzpaomykvc4pogw5ex53oceb4
Domain Adaptation Meets Disentangled Representation Learning and Style Transfer
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Besides, the trained network also demonstrates high potential to generate style-transferred images. ...
Conversely, the specific parts characterize the unique style of each individual domain. ...
In detail, we input a combined feature map (S S , C T ) into a generator G S to generate a source-style image. ...
arXiv:1712.09025v4
fatcat:w4japs2n2jdhno5k3p6ehjhsye
ESA‐CycleGAN: Edge feature and self‐attention based cycle‐consistent generative adversarial network for style transfer
2021
IET Image Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
To address these problems, an edge feature and self-attention based cycle-consistent generative adversarial network (ESA-CycleGAN) is proposed. ...
However, many existing methods of style transfer suffer from loss of details and poor overall visual effect. ...
adversarial network for style transfer. ...
doi:10.1049/ipr2.12342
fatcat:3tgzvrez7jaj3lglq562ap7o6e
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Here we tackle the more generic problem of semantic style transfer: given two unpaired collections of images, we aim to learn a mapping between the corpus-level style of each collection, while preserving ...
Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter. ...
Neural style transfer refers to the task of transferring the texture of a specific style image while preserving the pixel-level structure of an input content image [4, 9] . ...
arXiv:1711.05139v6
fatcat:z77otptkt5chba3p4tlolmhkm4
Real-Time Monocular Depth Estimation Using Synthetic Data with Domain Adaptation via Image Style Transfer
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
With advances in image style transfer and its connections with domain adaptation (Maximum Mean Discrepancy), we take advantage of style transfer and adversarial training to predict pixel perfect depth ...
Training a depth estimation model using pixel-perfect synthetic data can resolve most of these issues but introduces the problem of domain bias. ...
generator that can transfer a specific style (that of our synthetic domain in our work) onto a set of images of a specific domain (real-world images). ...
doi:10.1109/cvpr.2018.00296
dblp:conf/cvpr/AbarghoueiB18
fatcat:mqjagcr5afawrfit6hoo74dz5u
MISS GAN: A Multi-IlluStrator Style Generative Adversarial Network for image to illustration translation
2021
Pattern Recognition Letters
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print arXiv:2108.05693v1 fatcat:zijo5xk54rciplv3pmj6gdzeoy
This paper proposes a Multi-IlluStrator Style Generative Adversarial Network (MISS GAN) that is a multi-style framework for unsupervised image-to-illustration translation, which can generate styled yet ...
Unsupervised style transfer that supports diverse input styles using only one trained generator is a challenging and interesting task in computer vision. ...
The first is style transfer for a specific artistic style and the second is generating an arbitrary artistic style given a pair of content and style images. ...
doi:10.1016/j.patrec.2021.08.006
fatcat:oq4keyjfcbaj3jgdr77ocwtpmu
Deep Generative Adversarial Networks for Image-to-Image Translation: A Review
2020
Symmetry
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Image-to-image translation with generative adversarial networks (GANs) has been intensively studied and applied to various tasks, such as multimodal image-to-image translation, super-resolution translation ...
Such translation entails learning to map one visual representation of a given input to another representation. ...
Style Transfer Style transfer is the process of rendering the content of an image with a specific style while preserving the content, as shown in Figure 6 . ...
doi:10.3390/sym12101705
fatcat:rqlwjjhrvbc6fhc4mxjjvkwk6i
RSMT: A Remote Sensing Image-to-Map Translation Model via Adversarial Deep Transfer Learning
2022
Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Our remote sensing image-to-map translation model (RSMT) achieves universal and general applicability to generate maps over multiple regions by combining adversarial deep transfer training schemes with ...
The current translation methods for remote sensing image-to-map tasks only work on the specific regions with similar styles and structures to the training set and perform poorly on previously unseen areas ...
[34] introduce an adversarial framework called DANN to transfer information for domain adaption. ...
doi:10.3390/rs14040919
fatcat:ik7oiue2zbcl3p2wjsapbpuubm
Unpaired Domain Transfer for Data Augment in Face Recognition
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
., domain gap and training data shortage. We propose the unpaired Domain Transfer Generative Adversarial Network (DT-GAN) to relieve these two obstacles. ...
We improve the GAN baseline to bridge the domain gap among datasets by generating images conforming to the style of a target domain by learning the mapping between the source domain and target domain. ...
CycleGAN is a general solution for neural style transfer regardless of the identity of objects. ...
doi:10.1109/access.2020.2976207
fatcat:cbb6gnnzxngtje6q52o2wnbb7a
SingleGAN: Image-to-Image Translation by a Single-Generator Network using Multiple Generative Adversarial Learning
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
However, most recent methods require multiple generators for modeling different domain mappings, which are inefficient and ineffective on some multi-domain image translation tasks. ...
Besides, we explore variants of SingleGAN for different tasks, including one-to-many domain translation, many-to-many domain translation and one-to-one domain translation with multimodality. ...
As far as image style transfer is concerned, different style transfer from a single input image is a representative G A C B D G A B E Domain code Domain code + Random latent code (a) (c) G A C B D Domain ...
arXiv:1810.04991v2
fatcat:34ln4uqkpjan5fmjdnjbvcnt2e
Everyone is a Cartoonist: Selfie Cartoonization with Attentive Adversarial Networks
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, we address this problem by proposing a selfie cartoonization Generative Adversarial Network (scGAN), which mainly uses an attentive adversarial network (AAN) to emphasize specific facial ...
Experimental results show that our method is capable of generating different cartoon styles and outperforms a number of state-of-the-art methods. ...
There are another family of methods based on Generative Adversarial Networks (GAN) [4] that perform domain transfer in an adversarial manner. ...
arXiv:1904.12615v1
fatcat:i3rzdu3mu5cotb54e2mr2y2s2q
Joint Adversarial Learning for Domain Adaptation in Semantic Segmentation
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
For such a task, the key point is to learn domain-invariant representations and adversarial learning is usually used, in which the discriminator is to distinguish which domain the input comes from, and ...
Specifically, WTM changes the original decoder into a new decoder, which is learned only under the supervision of adversarial loss and thus mainly focuses on reducing domain divergence. ...
Specifically, WTM transfers the original decoder to a new one and the new decoder is only trained by adversarial loss. ...
doi:10.1609/aaai.v34i04.6169
fatcat:ebai25uifvaddmx3wcgyvrwdyy
Example-Guided Style-Consistent Image Synthesis From Semantic Labeling
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Figure 1 : We present a generative adversarial framework for synthesizing images from semantic label maps as well as image exemplars. ...
We propose a solution to the example-guided image synthesis problem using conditional generative adversarial networks with style consistency. ...
We thank the anonymous reviewers for the valuable discussions. This work was supported by the Natural Science Foundation of China (Project Number: 61521002, 61561146393). ...
doi:10.1109/cvpr.2019.00159
dblp:conf/cvpr/0004YLLZHH19
fatcat:plmskjeagbasbcxcoaz3fwsfsa
« Previous
Showing results 1 — 15 out of 14,573 results