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Domain-Specific Mappings for Generative Adversarial Style Transfer [article]

Hsin-Yu Chang, Zhixiang Wang, Yung-Yu Chuang
2020 arXiv   pre-print
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

S. Ramya, S. Anchana, A.M. Bavidhraa, R. Devanand
2020 International Journal of Computer Applications  
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

Guoyun Lv, Syed Muhammad Israr, Shengyong Qi
2021 IEEE Access  
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]

Hoang Tran Vu, Ching-Chun Huang
2018 arXiv   pre-print
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

Li Wang, Lidan Wang, Shubai Chen
2021 IET Image Processing  
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]

Amélie Royer, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole, Kevin Murphy
2018 arXiv   pre-print
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

Amir Atapour-Abarghouei, Toby P. Breckon
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
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

Noa Barzilay, Tal Berkovitz Shalev, Raja Giryes
2021 Pattern Recognition Letters  
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

Aziz Alotaibi
2020 Symmetry  
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

Jieqiong Song, Jun Li, Hao Chen, Jiangjiang Wu
2022 Remote Sensing  
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

Jinjin Liu, Qingbao Li, Ping Zhang, Guimin Zhang, Ming Liu
2020 IEEE Access  
., 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]

Xiaoming Yu, Xing Cai, Zhenqiang Ying, Thomas Li, Ge Li
2020 arXiv   pre-print
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]

Xinyu Li, Wei Zhang, Tong Shen, Tao Mei
2019 arXiv   pre-print
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

Yixin Zhang, Zilei Wang
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
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

Miao Wang, Guo-Ye Yang, Ruilong Li, Run-Ze Liang, Song-Hai Zhang, Peter M. Hall, Shi-Min Hu
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
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
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