Dynamically occluded samples via adversarial learning for person re-identification in sensor networks
Sign Up to like & getrecommendations! Published in 2021 at "Ad Hoc Networks"
DOI: 10.1016/j.adhoc.2020.102316
Abstract: Abstract In this paper, we propose a novel data augmentation method to dynamically learn occluded samples via adversarial learning for person re-identification (re-ID) in sensor networks. Specifically, we design two CNN models to learn original-image… read more here.
Keywords: image features; occluded samples; samples via; image ... See more keywords
Segmentation and quantification of infarction without contrast agents via spatiotemporal generative adversarial learning
Sign Up to like & getrecommendations! Published in 2020 at "Medical image analysis"
DOI: 10.1016/j.media.2019.101568
Abstract: Accurate and simultaneous segmentation and full quantification (all indices are required in a clinical assessment) of the myocardial infarction (MI) area are crucial for early diagnosis and surgical planning. Current clinical methods remain subject to… read more here.
Keywords: contrast; segmentation; infarction; segmentation quantification ... See more keywords
Domain adaptation based on domain-invariant and class-distinguishable feature learning using multiple adversarial networks
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.06.044
Abstract: Abstract Adversarial networks have been used to learn transferable representations in many domain adaptation methods. However, there is no theoretical guarantee that two distributions are identical, even if the discriminator is fully confused. Therefore, a… read more here.
Keywords: adversarial networks; class; domain adversarial; central samples ... See more keywords
Adversarial-learning-based image-to-image transformation: A survey
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.06.067
Abstract: Abstract Recently, the generative adversarial network (GAN) has attracted wide attention for various computer vision tasks. GAN provides a novel concept for image-to-image transformation by means of adversarial learning. In recent years, numerous adversarial-learning-based methods… read more here.
Keywords: image; image transformation; image image; learning based ... See more keywords
Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data
Sign Up to like & getrecommendations! Published in 2023 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btad096
Abstract: Abstract Motivation Untargeted metabolomics by mass spectrometry is the method of choice for unbiased analysis of molecules in complex samples of biological, clinical or environmental relevance. The exceptional versatility and sensitivity of modern high-resolution instruments… read more here.
Keywords: untargeted metabolomics; multi batch; regularized adversarial; adversarial learning ... See more keywords
Robust quantum control in games: An adversarial learning approach
Sign Up to like & getrecommendations! Published in 2020 at "Physical Review A"
DOI: 10.1103/physreva.101.052317
Abstract: High-precision operation of quantum computing systems must be robust to uncertainties and noises in the quantum hardware. In this paper, we show that through a game played between the uncertainties (or noises) and the controls,… read more here.
Keywords: adversarial learning; robust quantum; quantum control; approach ... See more keywords
Adversarial Learning for Invertible Steganography
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3034936
Abstract: Deep neural networks have revolutionised the research landscape of steganography. However, their potential has not been explored in invertible steganography, a special class of methods that permits the recovery of distorted objects due to steganographic… read more here.
Keywords: invertible steganography; method; steganography; learning invertible ... See more keywords
Collaborative Filtering Recommendation Algorithm Based on Attention GRU and Adversarial Learning
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3038770
Abstract: Aiming at the problem that the traditional collaborative filtering algorithm using shallow models cannot learn the deep features of users and items, and the recommendation model is very susceptible to the counter-interference of its parameters;… read more here.
Keywords: adversarial learning; model; collaborative filtering; recommendation ... See more keywords
Contrastive Adversarial Learning for Endomicroscopy Imaging Super-Resolution.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE journal of biomedical and health informatics"
DOI: 10.1109/jbhi.2023.3275563
Abstract: Endomicroscopy is an emerging imaging modality for real-time optical biopsy. One limitation of existing endomicroscopy based on coherent fibre bundles is that the image resolution is intrinsically limited by the number of fibres that can… read more here.
Keywords: endomicroscopy; learning endomicroscopy; super resolution; resolution ... See more keywords
Adversarial Learning Networks for FinTech Applications Using Heterogeneous Data Sources
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2021.3100742
Abstract: The dynamic property and increasing complexity are the key challenges for modeling financial technology (FinTech)-related applications such as stock markets. Over the years, a lot of inflexible predictive strategies have been proposed for predicting stock… read more here.
Keywords: fintech applications; networks fintech; learning networks; applications using ... See more keywords
Adversarial Learning Targeting Deep Neural Network Classification: A Comprehensive Review of Defenses Against Attacks
Sign Up to like & getrecommendations! Published in 2020 at "Proceedings of the IEEE"
DOI: 10.1109/jproc.2020.2970615
Abstract: With wide deployment of machine learning (ML)-based systems for a variety of applications including medical, military, automotive, genomic, multimedia, and social networking, there is great potential for damage from adversarial learning (AL) attacks. In this… read more here.
Keywords: neural network; defenses attacks; deep neural; adversarial learning ... See more keywords