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Informative Sample Mining Network for Multi-Domain Image-to-Image Translation
[article]
2020
arXiv
pre-print
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To select informative samples, we dynamically estimate sample importance during the training of Generative Adversarial Networks, presenting Informative Sample Mining Network. ...
The performance of multi-domain image-to-image translation has been significantly improved by recent progress in deep generative models. ...
In this paper, we propose Informative sample mining network (INIT) to enhance training efficiency and improve performance in multi-domain I2I tasks. ...
arXiv:2001.01173v4
fatcat:og7pixtnxngw5lyfdywika42ru
Exploring Patch-wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks
[article]
2022
arXiv
pre-print
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We verified our method for three tasks: single-modal and multi-modal image translations, and GAN compression task for image translation. ...
To further improve the performance, we present a hard negative mining by exploiting the semantic relation. ...
Multi-modal image translation For further evaluation, we apply our method to multimodal image translation model which is a framework for translating input to diverse outputs with multiple domains. ...
arXiv:2203.01532v1
fatcat:zbepagm2fngdjcdns7j4g4oeeu
Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translation
[article]
2021
arXiv
pre-print
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To address this issue, we present instance-wise hard Negative Example Generation for Contrastive learning in Unpaired image-to-image Translation (NEGCUT). ...
In this paper, we uncover that the negative examples play a critical role in the performance of contrastive learning for image translation. ...
It was also supported by the GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC. ...
arXiv:2108.04547v2
fatcat:n3vtsbceunbvzdrbxz2owmfrpe
Deep Learning for SAR-Optical Image Matching
2019
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
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Driven by the success of deep learning in conventional optical image matching, we have carried out extensive research with regard to deep matching for SAR-optical multi-sensor image pairs in the recent ...
underlying loss function, and creation of artificial images by generative adversarial networks. ...
Fig. 2 : 2 Fig. 2: Conditional GAN architecture: the generator network learns to translate images between domains, while the discriminator learns to distinguish generated and real image pairs. ...
doi:10.1109/igarss.2019.8898635
dblp:conf/igarss/HughesMBA019
fatcat:jtl3oq4gnfb5pahswozgs4sizq
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, ...
[168] propose the informative sample mining network (INIT) to analyze the importance of sample selection and select the informative samples for multihop training. Wu et al. ...
arXiv:2101.08629v2
fatcat:i6pywjwnvnhp3i7cmgza2slnle
GD-StarGAN: Multi-domain image-to-image translation in garment design
2020
PLoS ONE
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One of the image-to-image translation models--StarGAN, has realized the function of multi-domain image-to-image translation by using only a single generator and a single discriminator. ...
In the field of fashion design, designing garment image according to texture is actually changing the shape of texture image, and image-to-image translation based on Generative Adversarial Network (GAN ...
L1 loss is for two domains and is not suitable for multi-domain image-to-image translation. Thus, the domain classification loss is adopted in this paper to achieve cyclic consistency. ...
doi:10.1371/journal.pone.0231719
pmid:32315361
fatcat:sgcalmpzcjhftcfgpllycevryu
MCMI: Multi-Cycle Image Translation with Mutual Information Constraints
[article]
2020
arXiv
pre-print
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We present a mutual information-based framework for unsupervised image-to-image translation. ...
The proposed mutual information constraints can improve cross-domain mappings by optimizing out translation functions that fail to satisfy the Markov property during image translations. ...
Introduction Image-to-image (I2I) translation has gained prominence in recent years. The goal for I2I translation is to map an image from one domain to a corresponding image in another domain. ...
arXiv:2007.02919v1
fatcat:3xbmuymkhjf33nnxmeggysw44u
Biphasic Learning of GANs for High-Resolution Image-to-Image Translation
[article]
2019
arXiv
pre-print
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In this work, we present a novel training framework for GANs, namely biphasic learning, to achieve image-to-image translation in multiple visual domains at 1024^2 resolution. ...
and sample quality when applied to the high-resolution situation. ...
loss (denoted as "w/o mutual Info loss"), a network that maximizes mutual information as described in MINE [1] (denoted as "w/ MINE loss"), and a network that replaces mutual information loss with the ...
arXiv:1904.06624v1
fatcat:in6vb4wnczhhtbczsalm4rvwuy
Unsupervised Domain Adaptation of Object Detectors: A Survey
[article]
2021
arXiv
pre-print
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Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images, termed as domain adaptation problem. ...
There is a plethora of works to adapt classification and segmentation models to label-scarce target datasets through unsupervised domain adaptation. ...
[121] Adversarial feature learning
Image-to-image translation
Domain randomization Roychowdhury et al. [61] Khodabandeh et al. [62] Kim et al. [97] D'Innocente et al. ...
arXiv:2105.13502v2
fatcat:ozzbbvoflfdvjdewjnjmfajlpa
Leveraging Virtual and Real Person for Unsupervised Person Re-identification
[article]
2018
arXiv
pre-print
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To address this problem, this study introduces a novel approach for unsupervised person re-ID by leveraging virtual and real data. ...
For training of deep re-ID model, we divide it into three steps: 1) pre-training a coarse re-ID model by using virtual data; 2) collaborative filtering based positive pair mining from the real data; and ...
These two models can only transfer images from one domain to another and may not be flexible enough when dealing with multi-domain translation. ...
arXiv:1811.02074v1
fatcat:al26lwojuvbkbi2dviyls4zaye
A Multi-Domain Collaborative Transfer Learning Method with Multi-Scale Repeated Attention Mechanism for Underwater Side-Scan Sonar Image Classification
2022
Remote Sensing
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Using different characteristics of multi-domain data to efficiently capture useful features for the sonar image classification, MDCTL offers a new way for transfer learning. ...
In this paper, a multi-domain collaborative transfer learning (MDCTL) method with multi-scale repeated attention mechanism (MSRAM) is proposed for improving the accuracy of underwater sonar image classification ...
• The randomly generated samples with consistent distribution of the training dataset are created by the generative adversarial networks (GAN), which are trained to learn an image-translation from low-complexity ...
doi:10.3390/rs14020355
fatcat:wu734pxal5hrfd4fh4txeow7cq
Deep-sea Nodule Mineral Image Segmentation Algorithm Based on Pix2PixHD
2022
Computers Materials & Continua
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The model uses a coarse-to-fine generator composed of a global generation network and two local enhancement networks, and multiple multi-scale discriminators with same network structures but different ...
It is important for expanding the application of deep learning techniques in the field of deep-sea exploration and mining. ...
Acknowledgement: Thanks to other teachers and students in the Media Computing Laboratory of the Minzu University of China and anonymous reviewers for their valuable comments and contributions to this research ...
doi:10.32604/cmc.2022.027213
fatcat:tjnkzgrzmnabxjiku6vflwfsz4
SDTGAN: Generation Adversarial Network for Spectral Domain Translation of Remote Sensing Images of the Earth Background Based on Shared Latent Domain
2022
Remote Sensing
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The introduction of shared latent domain allows multi-spectral domains connect to each other without the need to build a one-to-one model. ...
Based on the shared latent domain hypothesis and generation adversarial network, this paper proposes the SDTGAN method to mine the correlation between the spectrum and directly generate target spectral ...
Informed Consent Statement: Not applicable.
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article. ...
doi:10.3390/rs14061359
fatcat:jbgcexogl5efdbdjwo4ggmulvy
Retrieval Guided Unsupervised Multi-domain Image-to-Image Translation
[article]
2020
arXiv
pre-print
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However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains ...
Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. ...
quality images from multi-modal and multi-domain image-to-image translations; • To our knowledge, we are the first to train a retrieval system exploiting image-to-image translation model generated images ...
arXiv:2008.04991v1
fatcat:q3kkauvhhzd23pqvkprt3ycwvi
DeepEDN: A Deep Learning-based Image Encryption and Decryption Network for Internet of Medical Things
[article]
2020
arXiv
pre-print
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Internet of Medical Things (IoMT) can connect many medical imaging equipments to the medical information network to facilitate the process of diagnosing and treating for doctors. ...
In order to facilitate the data mining directly from the privacy-protected environment, a region of interest(ROI)-mining-network is proposed to extract the interested object from the encrypted image. ...
The encryption network G attempts to generate an images G(x) similar to the image in domain Y , while the discriminator network D aims to find the difference between translated samples from G(x) and real ...
arXiv:2004.05523v2
fatcat:bck62nothzftzeqfkl7g6xq2ye
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