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Asymmetric GAN for Unpaired Image-to-image Translation

Yu Li, Sheng Tang, Rui Zhang, Yongdong Zhang, Jintao Li, Shuicheng Yan
2019 IEEE Transactions on Image Processing  

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2019 pre-print arXiv:1912.11660v1 fatcat:qafmz4rvined7k6mrveft43hhu
In conclusion, our AsymGAN provides a better solution for unpaired image-to-image translation in asymmetric domains.  ...  Unpaired image-to-image translation problem aims to model the mapping from one domain to another with unpaired training data.  ...  CONCLUSION In this paper, we propose an Asymmetric GAN approach for unpaired image-to-image translation focusing on the asymmetric situations.  ... 
doi:10.1109/tip.2019.2922854 fatcat:r57oavryczfwpfvcx2giopbd7u
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Yu Li, Sheng Tang, Rui Zhang, Yongdong Zhang, Jintao Li, Shuicheng Yan. "Asymmetric GAN for Unpaired Image-to-image Translation." IEEE Transactions on Image Processing 28.12 (2019) 1-1

Asymmetric Generative Adversarial Networks for Image-to-Image Translation [article]

Hao Tang, Dan Xu, Hong Liu, Nicu Sebe
2019 arXiv   pre-print

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State-of-the-art models for unpaired image-to-image translation with Generative Adversarial Networks (GANs) can learn the mapping from the source domain to the target domain using a cycle-consistency loss  ...  To the best of our knowledge, we are the first to investigate the asymmetric GAN framework on both unsupervised and supervised image-to-image translation tasks.  ...  We investigate four aspects of Asymmetric-GAN for the multi-domain image-to-image translation task. (1) Importance of Distinct Network Designs for Different Generators.  ... 
arXiv:1912.06931v1 fatcat:ndxh2ncq3na2zcd4bsseywlvmu
Citation

Hao Tang, Dan Xu, Hong Liu, Nicu Sebe. "Asymmetric Generative Adversarial Networks for Image-to-Image Translation." arXiv (2019)

Unsupervised Haze Removal for Aerial Imagery Based on Asymmetric Contrastive CycleGAN

Xin He, Wanfeng Ji, Jinpeng Xie
2022 IEEE Access  

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To this end, this paper aims to learn an effective unsupervised dehazing model from an unpaired set of clear and hazy aerial images.  ...  Motivated by the great advantages of contrastive learning in unsupervised representation field, we first attempt to formulate a Asymmetric Contrastive CycleGAN dehazing framework (namely ACC-GAN) to maximize  ...  CycleGAN [35] has been employed to address the unpaired image-to-image translation problem, to achieve an unsupervised dehazing process.  ... 
doi:10.1109/access.2022.3186004 fatcat:jyofn2dyazfnpfq4353rfbt3s4
DOAJ
Citation

Xin He, Wanfeng Ji, Jinpeng Xie. "Unsupervised Haze Removal for Aerial Imagery Based on Asymmetric Contrastive CycleGAN." IEEE Access (2022) 67316-67328

CrossNet: Latent Cross-Consistency for Unpaired Image Translation

Omry Sendik, Dani Lischinski, Daniel Cohen-Or
2020 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)  

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In this work, we introduce a novel architecture for unpaired image translation, and explore several new regularizers enabled by it.  ...  Figure 1 : Given two unpaired sets of images, we train a model to perform translation between the two sets.  ...  To address these issues, existing GAN-based approaches for unpaired image translation [28, 26] , train two GANs.  ... 
doi:10.1109/wacv45572.2020.9093322 dblp:conf/wacv/SendikLC20 fatcat:ehdntjb3v5e2xmwydg4kxaajzm
Citation

Omry Sendik, Dani Lischinski, Daniel Cohen-Or. "CrossNet: Latent Cross-Consistency for Unpaired Image Translation." 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) (2020) 3032-3040

Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping

Ran Yi, Yong-Jin Liu, Yu-Kun Lai, Paul L. Rosin
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  

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To address this problem, we propose a novel asymmetric cycle mapping that enforces the reconstruction information to be visible (by a truncation loss) and only embedded in selective facial regions (by  ...  In this paper, we address the problem of automatic transfer from face photos to portrait drawings with unpaired training data.  ...  Previous methods for unpaired image-to-image translation [25, 21] use a cycle structure to regularize training.  ... 
doi:10.1109/cvpr42600.2020.00824 dblp:conf/cvpr/YiLLR20 fatcat:gai3tdzdvvccpccf4mukbrjfje
Citation

Ran Yi, Yong-Jin Liu, Yu-Kun Lai, Paul L. Rosin. "Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping." 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020) 8214-8222

Conditional Image-to-Image Translation [article]

Jianxin Lin, Yingce Xia, Tao Qin, Zhibo Chen, Tie-Yan Liu
2018 arXiv   pre-print

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We tackle this problem with unpaired data based on GANs and dual learning.  ...  Image-to-image translation tasks have been widely investigated with Generative Adversarial Networks (GANs) and dual learning.  ...  Furthermore, cd-GAN works for both symmetric translations and asymmetric translations.  ... 
arXiv:1805.00251v1 fatcat:dz3ewuzmqfai5fbzuyhe5binrm
Citation

Jianxin Lin, Yingce Xia, Tao Qin, Zhibo Chen, Tie-Yan Liu. "Conditional Image-to-Image Translation." arXiv (2018)

Conditional Image-to-Image Translation

Jianxin Lin, Yingce Xia, Tao Qin, Zhibo Chen, Tie-Yan Liu
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  

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We tackle this problem with unpaired data based on GANs and dual learning.  ...  Image-to-image translation tasks have been widely investigated with Generative Adversarial Networks (GANs) and dual learning.  ...  Furthermore, cd-GAN works for both symmetric translations and asymmetric translations.  ... 
doi:10.1109/cvpr.2018.00579 dblp:conf/cvpr/LinXQ0L18 fatcat:aghknif7mvghdmr6k6ztfjkovi
Citation

Jianxin Lin, Yingce Xia, Tao Qin, Zhibo Chen, Tie-Yan Liu. "Conditional Image-to-Image Translation." 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018) 5524-5532

NIR to RGB Domain Translation Using Asymmetric Cycle Generative Adversarial Networks

Tian Sun, Cheolkon Jung, Qingtao Fu, Qihui Han
2019 IEEE Access  

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We adopt asymmetric cycle GANs that have different network capacities according to the translation direction.  ...  Near infrared (NIR) images have clear textures but do not contain color. In this paper, we propose NIR to RGB domain translation using asymmetric cycle generative adversarial networks (ACGANs).  ...  ACKNOWLEDGMENT The authors would like to thank Dr. Chen Su and Dr. Hubert Liu in Huawei Technologies for their helpful advice and discussion.  ... 
doi:10.1109/access.2019.2933671 fatcat:7ucq6577pvaqjo3khugdwm6gmu
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Citation

Tian Sun, Cheolkon Jung, Qingtao Fu, Qihui Han. "NIR to RGB Domain Translation Using Asymmetric Cycle Generative Adversarial Networks." IEEE Access (2019) 1-1

Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning [article]

Xiang Chen, Zhentao Fan, Pengpeng Li, Longgang Dai, Caihua Kong, Zhuoran Zheng, Yufeng Huang, Yufeng Li
2022 arXiv   pre-print

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2022 pre-print
arXiv:2203.07677v1 fatcat:exohmuvihvbzrpaoettq6kwiju
We offer a practical unpaired learning based image dehazing network from an unpaired set of clear and hazy images.  ...  With such formulation, the proposed contrastive disentangled dehazing method (CDD-GAN) employs negative generators to cooperate with the encoder network to update alternately, so as to produce a queue  ...  Due to the fact that the domain knowledge of the hazy and haze-free images is asymmetrical, it is laborious for these CycleGAN-based strategies to capture accurate mapping between two different domains  ... 
arXiv:2203.07677v2 fatcat:gsivtwvqfbdlve6ttnqwdg26vq
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Xiang Chen, Zhentao Fan, Pengpeng Li, Longgang Dai, Caihua Kong, Zhuoran Zheng, Yufeng Huang, Yufeng Li. "Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning." arXiv (2022)

The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN [article]

Wenjun Yan, Yuanyuan Wang, Shengjia Gu, Lu Huang, Fuhua Yan, Liming Xia, Qian Tao
2019 arXiv   pre-print

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In this work, we proposed a generic framework to address this problem, consisting of (1) an unpaired generative adversarial network (GAN) for vendor-adaptation, and (2) a Unet for object segmentation.  ...  The proposed Unet-GAN provides an annotation-free solution to the cross-vendor medical image segmentation problem, potentially extending a trained deep learning model to multi-center and multi-vendor use  ...  LV-Unet follows an asymmetric encoder-decoder structure as illustrated in Fig.2 (a) . CycleGAN. CycleGAN is an established architecture designed for unpaired image-toimage translation [8] .  ... 
arXiv:1910.13681v1 fatcat:sts3q6dfffgrhhwocwawem4dfm
Citation

Wenjun Yan, Yuanyuan Wang, Shengjia Gu, Lu Huang, Fuhua Yan, Liming Xia, Qian Tao. "The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN." arXiv (2019)

Deep Generative Adversarial Networks for Image-to-Image Translation: A Review

Aziz Alotaibi
2020 Symmetry  

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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  ...  This article provides a comprehensive overview of image-to-image translation based on GAN algorithms and its variants.  ...  More recently, AsymGAN [85] has been proposed; it uses an asymmetric framework to model unpaired image-to-image translation between asymmetric domains by adding an auxiliary variable (aux).  ... 
doi:10.3390/sym12101705 fatcat:rqlwjjhrvbc6fhc4mxjjvkwk6i
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Aziz Alotaibi. "Deep Generative Adversarial Networks for Image-to-Image Translation: A Review." Symmetry 12.10 (2020) 1705

Unpaired Deep Image Deraining Using Dual Contrastive Learning [article]

Xiang Chen, Jinshan Pan, Kui Jiang, Yufeng Li, Yufeng Huang, Caihua Kong, Longgang Dai, Zhentao Fan
2022 arXiv   pre-print

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2021 pre-print
arXiv:2109.02973v1 fatcat:4lrzxaoafbgkpd6y5x6hv3c4qe
2021 pre-print
arXiv:2109.02973v2 fatcat:jfm4fljppvfbxgfrrwbta26z3m
2022 pre-print
arXiv:2109.02973v3 fatcat:f5ajsm24lbacrpz3bdgziiuloy
as DCD-GAN.  ...  rainy inputs to clean outputs as there exists significant domain gap between the rainy and clean images.  ...  We note that [52] introduces a cycle-consistent constraint for unpaired image-to-image translation.  ... 
arXiv:2109.02973v4 fatcat:i5rmbjewuvgcxnklm6nfaadbmq
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Xiang Chen, Jinshan Pan, Kui Jiang, Yufeng Li, Yufeng Huang, Caihua Kong, Longgang Dai, Zhentao Fan. "Unpaired Deep Image Deraining Using Dual Contrastive Learning." arXiv (2022)

An Asymmetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans [article]

Michael Gadermayr, Maximilian Tschuchnig, Laxmi Gupta, Dorit Merhof, Nils Krämer, Daniel Truhn, Burkhard Gess
2021 arXiv   pre-print

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2020 pre-print
arXiv:2004.11001v2 fatcat:nv3xif2vsrgolebjzksajyewym
2020 pre-print
arXiv:2004.11001v1 fatcat:fbbiwhzfsrcurk4u5ddb3t2aoa
Generative adversarial networks using a cycle-consistency loss facilitate unpaired training of image-translation models and thereby exhibit a very high potential in manifold medical applications.  ...  However, the fact that images in one domain potentially map to more than one image in another domain (e.g. in case of pathological changes) exhibits a major challenge for training the networks.  ...  Several approaches have so far been proposed to tackle the one-to-many mapping problem in unpaired image-translation settings: Almahairi et al. [7] and Huang et al.  ... 
arXiv:2004.11001v3 fatcat:iiexwoe7anhz5mpwobdjktt7r4
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Michael Gadermayr, Maximilian Tschuchnig, Laxmi Gupta, Dorit Merhof, Nils Krämer, Daniel Truhn, Burkhard Gess. "An Asymmetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans." arXiv (2021)

Learning Temporally and Semantically Consistent Unpaired Video-to-video Translation Through Pseudo-Supervision From Synthetic Optical Flow [article]

Kaihong Wang, Kumar Akash, Teruhisa Misu
2022 arXiv   pre-print

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2022 pre-print
arXiv:2201.05723v1 fatcat:fnpwntcmmzfste4h6jhoommumq
2022 pre-print
arXiv:2201.05723v2 fatcat:xw5gt3t7tnhdtorqarhx6ve5yu
Unpaired video-to-video translation aims to translate videos between a source and a target domain without the need of paired training data, making it more feasible for real applications.  ...  However, the inaccuracies in the estimation of motion deteriorate the quality of the guidance towards spatiotemporal consistency, which leads to unstable translation.  ...  Consequently, unpaired image-to-image translation (Zhu et al. 2017a ) addresses this task in the absence of paired images.  ... 
arXiv:2201.05723v3 fatcat:fmug73pzj5ghhjxsjx7ctcvnie
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Kaihong Wang, Kumar Akash, Teruhisa Misu. "Learning Temporally and Semantically Consistent Unpaired Video-to-video Translation Through Pseudo-Supervision From Synthetic Optical Flow." arXiv (2022)

Unsupervised Enhancement of Real-World Depth Images Using Tri-Cycle GAN [article]

Alona Baruhov, Guy Gilboa
2020 arXiv   pre-print

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We show that the resulting framework dramatically improves over the original Cycle-GAN both visually and quantitatively, extending its applicability to more challenging and asymmetric translation tasks  ...  In this work we aim to enhance highly degraded, real-world depth images acquired by a low-cost sensor, for which an analytical noise model is unavailable.  ...  To address this, we propose using an asymmetric loss function for promoting information preservation, which does not require the two translations to be inverses.  ... 
arXiv:2001.03779v1 fatcat:k7iwxaxqu5e57j5bc6f2hztw7i
Citation

Alona Baruhov, Guy Gilboa. "Unsupervised Enhancement of Real-World Depth Images Using Tri-Cycle GAN." arXiv (2020)

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