Suchergebnisse - "adversarial autoencoder (AAE)"

AAEGAN Loss Optimizations Supporting Data Augmentation on Cerebral Organoid Bright-field Images

Autoren: Brémond Martin, Clara Chane, Camille Clouchoux, Cedric et al.

Weitere Verfasser: Brémond Martin, Clara Chane, Camille Clouchoux, Cedric et al.

Quelle: Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. :307-314

Schlagwörter: 0301 basic medicine, Adversarial Autoencoder (AAE), 03 medical and health sciences, Segmentation, 0302 clinical medicine, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Generation, Loss, Cerebral Organoid, t-SNE, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing

Zugangs-URL: https://hal.science/hal-03528008v1

FMNet: Latent Feature-Wise Mapping Network for Cleaning Up Noisy Micro-Doppler Spectrogram

Autoren: Chong Tang Wenda Li Shelly Vishwakarma et al.

Quelle: IEEE Transactions on Geoscience and Remote Sensing. 60:1-12

Schlagwörter: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Activity Classification, Feature Mapping, 02 engineering and technology, Activity classification, Machine Learning (cs.LG), Variational Autoencoder, deep learning (DL), Deep Learning, passive WiFi radar (PWR), feature mapping, micro-Doppler spectrogram (μ-DS), FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, adversarial autoencoder (AAE), variational autoencoder (VAE), Electrical Engineering and Systems Science - Signal Processing, Micro-Doppler Spectrogram, Adversarial Autoencoder, Passive WiFi Radar

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/pdf/2107.07312
http://arxiv.org/abs/2107.07312
https://ieeexplore.ieee.org/document/9583945
https://dblp.uni-trier.de/db/journals/corr/corr2107.html#abs-2107-07312
https://www.arxiv-vanity.com/papers/2107.07312/
https://discovery-pp.ucl.ac.uk/id/eprint/10136070/

A Hybrid Autoencoder Network for Unsupervised Image Clustering

Autoren: Pei-Yin Chen Jih-Jeng Huang

Quelle: Algorithms, Vol 12, Iss 6, p 122 (2019)

Schlagwörter: image clustering, convolutional autoencoder (CAE), adversarial autoencoder (AAE), stacked autoencoder (SAE), Industrial engineering. Management engineering, T55.4-60.8, Electronic computers. Computer science, QA75.5-76.95

Relation: https://www.mdpi.com/1999-4893/12/6/122; https://doaj.org/toc/1999-4893; https://doaj.org/article/caae9c3398a34723a735c3518ada5b7f

Verfügbarkeit: https://doi.org/10.3390/a12060122
https://doaj.org/article/caae9c3398a34723a735c3518ada5b7f

Deep Learning-Based Probabilistic Autoencoder for Residential Energy Disaggregation: an Adversarial Approach

Autoren: Terriche, Yacine Çimen, Halil Vasquez, Juan C. et al.

Schlagwörter: Training, Hidden Markov models, Probabilistic logic, Analytical models, Generative adversarial networks, Deep learning, Data models, Adversarial autoencoder (AAE), energy disaggregation, nonintrusive load monitoring (NILM), online energy disaggregation, probabilistic energy disaggregation, residential energy disaggregation, Load Disaggregation

Relation: IEEE Transactions On Industrial Informatics; Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı; https://doi.org/10.1109/TII.2022.3150334; https://hdl.handle.net/20.500.13091/3155; 18; 12; 8399; 8408; WOS:000862429800007; Q1

Verfügbarkeit: https://hdl.handle.net/20.500.13091/3155
https://doi.org/10.1109/TII.2022.3150334

Deep learning based automated latent fingerprint identification

Autoren: Zhu, Yanming

Schlagwörter: Directed acyclic convolutional neural network (DACNN), Latent fingerprints, Adversarial autoencoder (AAE)

Dateibeschreibung: application/pdf

Relation: http://hdl.handle.net/1959.4/63255; https://doi.org/10.26190/unsworks/3764

Verfügbarkeit: http://hdl.handle.net/1959.4/63255
https://unsworks.unsw.edu.au/bitstreams/f310135c-7ac5-4a19-a824-e753954847a4/download
https://doi.org/10.26190/unsworks/3764