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Attribute Guided Unpaired Image-to-Image Translation with Semi-supervised Learning
[article]
2019
arXiv
pre-print
Preserved Fulltext
Especially, AGUIT benefits from two-fold: (1) It adopts a novel semi-supervised learning process by translating attributes of labeled data to unlabeled data, and then reconstructing the unlabeled data ...
Unpaired Image-to-Image Translation (UIT) focuses on translating images among different domains by using unpaired data, which has received increasing research focus due to its practical usage. ...
In this paper, we achieve the above goals in a unified framework by proposing an Attribute Guided Unpaired Image-to-image Translation (AGUIT) model. ...
arXiv:1904.12428v1
fatcat:vifa3uqt5ralrb7pdnd2ojrzii
Exploring Stroke-Level Modifications for Scene Text Editing
[article]
2022
arXiv
pre-print
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Secondly, we propose a Semi-supervised Hybrid Learning to train the network with both labeled synthetic images and unpaired real scene text images. ...
Previous methods of editing the whole image have to learn different translation rules of background and text regions simultaneously. 2) Domain gap. ...
Semi-supervised Hybrid Learning To adapt the model to real-world environments, we propose the Semi-supervised Hybrid Learning (SHL). ...
arXiv:2212.01982v1
fatcat:xjxsqb53nna4bg6gzmqegqd63m
Image-to-Image Translation: Methods and Applications
[article]
2021
arXiv
pre-print
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Image-to-image translation (I2I) aims to transfer images from a source domain to a target domain while preserving the content representations. ...
I2I has drawn increasing attention and made tremendous progress in recent years because of its wide range of applications in many computer vision and image processing problems, such as image synthesis, ...
[185] propose an attribute guided I2I (AGUIT) model that is the first work to handle multimodal and multidomain I2I with semi-supervised learning. AGUIT is trained following three steps. ...
arXiv:2101.08629v2
fatcat:i6pywjwnvnhp3i7cmgza2slnle
Semi-Supervised Image-to-Image Translation using Latent Space Mapping
[article]
2022
arXiv
pre-print
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We therefore introduce a general framework for semi-supervised image translation. ...
Recent image-to-image translation works have been transferred from supervised to unsupervised settings due to the expensive cost of capturing or labeling large amounts of paired data. ...
Therefore, the interest of this paper is in semi-supervised image-to-image translation. ...
arXiv:2203.15241v1
fatcat:hf7fulm6srdoremwijjwedsxva
Optimal Transport-Guided Conditional Score-Based Diffusion Models
[article]
2023
arXiv
pre-print
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Extensive experiments on unpaired super-resolution and semi-paired image-to-image translation demonstrated the effectiveness of the proposed OTCS model. ...
To tackle the applications with partially paired or even unpaired dataset, we propose a novel Optimal Transport-guided Conditional Score-based diffusion model (OTCS) in this paper. ...
Therefore, it is important and valuable to develop SBDMs for applications with only unpaired or partially paired training data, e.g., unpaired [16] or semi-paired [17] image-to-image translation (I2I ...
arXiv:2311.01226v1
fatcat:7j4tub43qna6bncn3pbisloam4
Semi-Supervised Learning for Few-Shot Image-to-Image Translation
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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To do so, we propose applying semi-supervised learning via a noise-tolerant pseudo-labeling procedure. ...
In the last few years, unpaired image-to-image translation has witnessed remarkable progress. ...
Conclusions We proposed semi-supervised learning to perform fewshot unpaired I2I translation with fewer image labels for the source domain. ...
doi:10.1109/cvpr42600.2020.00451
dblp:conf/cvpr/WangKG0K20
fatcat:xe2mszvufjf23ait7ly4slxkcu
ASL to PET Translation by a Semi-supervised Residual-based Attention-guided Convolutional Neural Network
[article]
2021
arXiv
pre-print
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To tackle this problem, we present a new semi-supervised multitask CNN which is trained on both paired data, i.e. ...
from 2D ASL and T1-weighted images to PET data. ...
ACKNOWLEDGEMENTS We are very grateful to the Amsterdam University Medical Center location VUmc for acquiring the PET-data of this study. We especially acknowledge the help of Prof.dr. ...
arXiv:2103.05116v1
fatcat:xcuuyi44zvbtxlcsqta6pkrcue
Semi-supervised Learning Approach to Generate Neuroimaging Modalities with Adversarial Training
[article]
2019
arXiv
pre-print
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In this work, we develop a method to address these issues with semi-supervised learning in translating between two neuroimaging modalities. ...
Our proposed model, Semi-Supervised Adversarial CycleGAN (SSA-CGAN), uses an adversarial loss to learn from unpaired data points, cycle loss to enforce consistent reconstructions of the mappings and another ...
Our method uses multiple adversarial signals for semi-supervised bi-directional image translation. ...
arXiv:1912.04391v1
fatcat:x6iyb7stjreezjayzmli2klb3e
Semi-supervised Learning Approach to Generate Neuroimaging Modalities with Adversarial Training
[chapter]
2020
Lecture Notes in Computer Science
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Our proposed model, Semi-Supervised Adversarial CycleGAN (SSA-CGAN), uses an adversarial loss to learn from unpaired data points, cycle loss to enforce consistent reconstructions of the mappings and another ...
In this work, we develop a method to address these issues with semisupervised learning in translating between two neuroimaging modalities. ...
Our proposed method, the Semi-Supervised Adversarial CycleGAN (SSA-CGAN ) further extends the application of leveraging unpaired data and paired data to MRI image translation, where the dimensionality ...
doi:10.1007/978-3-030-47436-2_31
fatcat:wa3ang7pujhb3f23gtfkoze7xq
Rethinking the Truly Unsupervised Image-to-Image Translation
[article]
2021
arXiv
pre-print
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Furthermore, TUNIT can be easily extended to semi-supervised learning with a few labeled data. ...
To this end, we propose a truly unsupervised image-to-image translation model (TUNIT) that simultaneously learns to separate image domains and translates input images into the estimated domains. ...
More importantly, it serves as a strong baseline to develop the semi-supervised image translation models. ...
arXiv:2006.06500v2
fatcat:kjmqznxnevgwbgod5xpiqwwqxi
Semi-supervised Learning for Few-shot Image-to-Image Translation
[article]
2020
arXiv
pre-print
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To do so, we propose applying semi-supervised learning via a noise-tolerant pseudo-labeling procedure. ...
In the last few years, unpaired image-to-image translation has witnessed remarkable progress. ...
Conclusions We proposed semi-supervised learning to perform fewshot unpaired I2I translation with fewer image labels for the source domain. ...
arXiv:2003.13853v2
fatcat:foomuq2hv5aqrpnx3wk2ul44oy
Rethinking Real-world Image Deraining via An Unpaired Degradation-Conditioned Diffusion Model
[article]
2024
arXiv
pre-print
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2023 pre-print arXiv:2301.09430v3 fatcat:dngm2f2dcrgzdgpyrogpvi2z3mOther Versions
We address the first challenge by introducing a stable and non-adversarial unpaired cycle-consistent architecture that can be trained, end-to-end, with only unpaired data for supervision; and the second ...
Extensive experiments confirm the superiority of our RainDiff over existing unpaired/semi-supervised methods and show its competitive advantages over several fully-supervised ones. ...
From (a) to (f): (a) the real rainy image, and the supervised learning results of (b) MSPFN; the semi-supervised learning results of (c) Syn2Real; the unsupervised results of (d) CycleGAN, (e) NLCL and ...
arXiv:2301.09430v4
fatcat:5opu22xdlfdrnmtr3yft2kaaru
Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning
[article]
2022
arXiv
pre-print
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We offer a practical unpaired learning based image dehazing network from an unpaired set of clear and hazy images. ...
images to be associated with latent factors. ...
guide the generated images to be associated with latent factors, so that we can facilitate the learned representation to fulfill factor disentanglement during the bidirectional translation process. ...
arXiv:2203.07677v2
fatcat:gsivtwvqfbdlve6ttnqwdg26vq
Implicit Pairs for Boosting Unpaired Image-to-Image Translation
[article]
2020
arXiv
pre-print
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In image-to-image translation the goal is to learn a mapping from one image domain to another. In the case of supervised approaches the mapping is learned from paired samples. ...
As a result, in recent years more attention has been given to techniques that learn the mapping from unpaired sets. ...
Several methods bridge the difference between supervised and unsupervised architectures by allowing the use of a small set of paired images, together with a large set of unpaired ones in a semi-supervised ...
arXiv:1904.06913v3
fatcat:ua3ww5biubbchhuyohx4sfpsya
DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:1912.07015v3 fatcat:knrxm6fv6rggrex2pyuxpqwvbiOther Versions
Specifically, we design an unsu-pervised attention guided rain streak extractor (U-ARSE) that utilizes a memory to extract the rain streak masks with two constrained cycle-consistency branches jointly ...
In this paper, we explore the unsupervised SID task using unpaired data and propose a novel net called Attention-guided Deraining by Constrained CycleGAN (or shortly, DerainCycleGAN), which can fully utilize ...
. (2) To extract the rain streaks from rainy images accurately, a new and unsupervised attention guided rain streak extractor (U-ARSE) is presented, which can learn the rain streak masks using unpaired ...
arXiv:1912.07015v4
fatcat:egqw5hd33jchvjgk62umo76rwm
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