Results 91 to 100 of about 13,735 (297)
Multi-modal data generation with a deep metric variational autoencoder [PDF]
, 2023 We present a deep metric variational autoencoder for multi-modal data generation. The variational autoencoder employs triplet loss in the latent space, which allows for conditional data generation by sampling new embeddings in the latent space within ...
Hannemose, Morten Rieger +7 more
core +1 more source
Continual Learning for Multimodal Data Fusion of a Soft Gripper
Advanced Robotics Research, EarlyView.Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley +1 more source
Energy Reports, 2021 Anomaly detection for hydropower turbine unit is a requirement for the safety of hydropower system. An unsupervised anomaly detection method employing variational modal decomposition (VMD) and deep autoencoder is proposed.
Hongteng Wang +3 more
doaj +1 more source
Certifiably Robust Variational Autoencoders
CoRR, 2021 12 pages and ...
Ben Barrett +3 more
openaire +3 more sources
Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
Frontiers in MicrobiologyIntroductionThe identification of microbe–drug associations can greatly facilitate drug research and development. Traditional methods for screening microbe-drug associations are time-consuming, manpower-intensive, and costly to conduct, so computational ...
Bo Wang +6 more
doaj +1 more source
Discrete Variational Autoencoders
CoRR, 2016 Probabilistic models with discrete latent variables naturally capture datasets composed of discrete classes. However, they are difficult to train efficiently, since backpropagation through discrete variables is generally not possible. We present a novel method to train a class of probabilistic models with discrete latent variables using the variational
openaire +3 more sources
Solid Harmonic Wavelet Bispectrum for Image Analysis
Advanced Science, EarlyView.The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown +3 more
wiley +1 more source
Disentangled conditional variational autoencoder for unsupervised anomaly detection [PDF]
, 2022 The goal of efficient anomaly or outlier detection is to learn the hidden representation of the data by identifying independent factors and minimizing information loss.
Neloy, Asif Ahmed
core
Extended Autoencoder for Novelty Detection with Reconstruction along Projection Pathway
Applied Sciences, 2020 Recently, novelty detection with reconstruction along projection pathway (RaPP) has made progress toward leveraging hidden activation values. RaPP compares the input and its autoencoder reconstruction in hidden spaces to detect novelty samples ...
Seung Yeop Shin, Han-joon Kim
doaj +1 more source