Results 61 to 70 of about 82,326 (277)

Unsupervised Domain Adaptation for Low-Dose Computed Tomography Denoising

IEEE Access, 2022
Deep neural networks have shown great improvements in low-dose computed tomography (CT) denoising. Early deep learning-based low-dose CT denoising algorithms were primarily based on supervised learning.
Jaa-Yeon Lee, Wonjin Kim, Yebin Lee, Ji-Yeon Lee, Eunji Ko, Jang-Hwan Choi   +5 more
doaj   +1 more source

Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos

, 2017
Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face recognition, due to the vast difference in visual quality between the domains and the difficulty of ...
Chandraker, Manmohan, Liu, Sifei, Sohn, Kihyuk, Yang, Ming-Hsuan, Yu, Xiang, Zhong, Guangyu   +5 more
core   +1 more source

Redefining Therapies for Drug‐Resistant Tuberculosis: Synergistic Effects of Antimicrobial Peptides, Nanotechnology, and Computational Design

Advanced Healthcare Materials, EarlyView.
Antimicrobial peptide (AMP)‐loaded nanocarriers provide a multifunctional strategy to combat drug‐resistant Mycobacterium tuberculosis. By enhancing intracellular delivery, bypassing efflux pumps, and disrupting bacterial membranes, this platform restores phagolysosome fusion and macrophage function.
Christian S. Carnero Canales, Jessica Ingrid Marquez Cazorla, Renzo Marianito Marquez Cazorla, Aline Martins dos Santos, Jonatas Lobato Duarte, Letícia Oliveira Catarin Nunes, Túlio Custódio Reis, Lara Cerazi Salvador, Norival Alves Santos‐Filho, Rafael Miguel Sábio, Hélder A. Santos, Fernando Rogério Pavan   +11 more
wiley   +1 more source

Structure preserved ordinal unsupervised domain adaptation

Electronic Research Archive
Unsupervised domain adaptation (UDA) aims to transfer the knowledge from labeled source domain to unlabeled target domain. The main challenge of UDA stems from the domain shift between the source and target domains.
Qing Tian, Canyu Sun
doaj   +1 more source

Cross-Device Profiled Side-Channel Attack with Unsupervised Domain Adaptation

Transactions on Cryptographic Hardware and Embedded Systems, 2021
Deep learning (DL)-based techniques have recently proven to be very successful when applied to profiled side-channel attacks (SCA). In a real-world profiled SCA scenario, attackers gain knowledge about the target device by getting access to a similar ...
Pei Cao, Chi Zhang, Xiangjun Lu, Dawu Gu
doaj   +1 more source

A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification

, 2019
Unsupervised cross-domain person re-identification (Re-ID) faces two key issues. One is the data distribution discrepancy between source and target domains, and the other is the lack of labelling information in target domain.
Gao, Yang, Huo, Jing, Qi, Lei, Shi, Yinghuan, Wang, Lei, Zhou, Luping   +5 more
core   +1 more source

ConDA: Continual Unsupervised Domain Adaptation

, 2021
10pages, 4 ...
Taufique, Abu Md Niamul, Jahan, Chowdhury Sadman, Savakis, Andreas   +2 more
openaire   +2 more sources

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

Advanced Materials, EarlyView.
AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll, Ivan Pinto‐Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll, Payam Habibzadeh Kavkani, Maria Chiara Spadaro, Yann‐Michel Niquet, Martin Børstad Eriksen, Sara Martí‐Sánchez, Georgios Katsaros, Giordano Scappucci, Peter Krogstrup, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Pablo Ordejón, Stephan Roche, Vincenzo Grillo, Jordi Arbiol   +19 more
wiley   +1 more source

Unsupervised Domain Adaptation by Mapped Correlation Alignment

IEEE Access, 2018
The goal of unsupervised domain adaptation aims to utilize labeled data from source domain to annotate the target-domain data, which has none of the labels.
Yun Zhang, Nianbin Wang, Shaobin Cai, Lei Song   +3 more
doaj   +1 more source

Dynamic Adaptation on Non-Stationary Visual Domains

, 2018
Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets.
B Moiseev, B Sun, C Tan, LC Chen, M Ghifary, M Tennant, SR Richter, T Tommasi, Y Ganin, Y LeCun, Y Wang   +10 more
core   +1 more source