Variational Inference via Rényi Bound Optimization and Multiple-Source Adaptation by Dana Zalman (Oshri), Shai Fine

“…We present a set of experiments, designed to evaluate the new VRLU bound and to compare the VRS method with the classic Variational Autoencoder (VAE) and the VR methods. Next, we apply the VRS approximation to the Multiple-Source Adaptation problem (MSA). …”
Get full text

L’IA, un outil de diagnostic pour le contrôle en ligne par radiographie industrielle by Jean-Robert Philippe

“…. ▪ La première de ces deux approches consiste à apprendre à un modèle de type Variational Autoencoder un pattern de « normalité », de la structure du matériau analysé, permettant ainsi de s’affranchir de l’apprentissage des défauts. ▪ La deuxième de ces deux approches consiste par des moyens de segmentation sémantique à détecter des défauts sur un produit à sur l’image. …”
Get full text

Enhancing Critical Infrastructure Security: Unsupervised Learning Approaches for Anomaly Detection by Andrea Pinto, Luis-Carlos Herrera, Yezid Donoso, Jairo A. Gutierrez

“…The final model combines the reconstruction ability of the variational autoencoder (VAE) with regularization using the Kullback–Leibler divergence, reflecting the non-Gaussian nature of industrial system data. …”
Get full text

Exploring the Evolution of Metal Halide Perovskites via Latent Representations of the Photoluminescent Spectra by Sheryl Sanchez, Yongtao Liu, Jonghee Yang, Sergei V. Kalinin, Maxim Ziatdinov, Mahshid Ahmadi

“…Herein, the binary library of metal halide perovskite (MHP) microcrystals, MAxFA1−xPbI3−xBrx, is explored via low‐dimensional latent representations of composition‐ and time‐dependent photoluminescence (PL) spectra. The variational autoencoder (VAE) approach is used to discover the latent factors of variability in the system. …”
Get full text

A Robust Approach Assisted by Signal Quality Assessment for Fetal Heart Rate Estimation from Doppler Ultrasound Signal by Xintong Shi, Natsuho Niida, Kohei Yamamoto, Tomoaki Ohtsuki, Yutaka Matsui, Kazunari Owada

“…Furthermore, we tackle the challenge of low-quality signals impacting FHR estimation by introducing a DUS SQA method based on unsupervised representation learning. We employ a variational autoencoder (VAE) to train representations of pre-processed fetal DUS data and aggregate them into a signal quality index (SQI) using a self-organizing map (SOM). …”
Get full text

A Marine Hydrographic Station Networks Intrusion Detection Method Based on LCVAE and CNN-BiLSTM by Tianhao Hou, Hongyan Xing, Xinyi Liang, Xin Su, Zenghui Wang

“…This paper proposes an NID method combining the log-cosh conditional variational autoencoder (LCVAE) with convolutional the bi-directional long short-term memory neural network (LCVAE-CBiLSTM) based on deep learning (DL). …”
Get full text

Deep kernel methods learn better: from cards to process optimization by Mani Valleti, Rama K Vasudevan, Maxim A Ziatdinov, Sergei V Kalinin

“…In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). …”
Get full text

Machine learning-assisted high-throughput exploration of interface energy space in multi-phase-field model with CALPHAD potential by Vahid Attari, Raymundo Arroyave

“…Furthermore, we use variational autoencoder, a deep generative neural network method, and label spreading, a semi-supervised machine learning method for establishing correlations between inputs of outputs of the computational model. …”
Get full text

Obfuscation Algorithm for Privacy-Preserving Deep Learning-Based Medical Image Analysis by Andreea Bianca Popescu, Ioana Antonia Taca, Anamaria Vizitiu, Cosmin Ioan Nita, Constantin Suciu, Lucian Mihai Itu, Alexandru Scafa-Udriste

“…We propose an image obfuscation algorithm that combines a variational autoencoder (VAE) with random non-bijective pixel intensity mapping to protect the content of medical images, which are subsequently employed in the development of DL-based solutions. …”
Get full text

A CVAE-Based Anomaly Detection Algorithm for Cyber Physical Attacks for Water Distribution Systems by Hajar Hameed Addeen, Yang Xiao, Tieshan Li

“…Therefore, this paper proposes a model based on a deep learning algorithm called a Conditional variational Autoencoder (CVAE) to disclose CPAs and mitigate their bad effects on WDS. …”
Get full text

Parameter Calibration and Verification of Elastoplastic Wet Sand Based on Attention-Retention Fusion Deep Learning Mechanism by Zhicheng Hu, Xianning Zhao, Junjie Zhang, Sibo Ba, Zifeng Zhao, Xuelin Wang

“…We propose a Parameter calibration neural network based on Attention, Retention, and improved Transformer for Sequential data (PartsNet), which effectively captures the nonlinear mechanical behavior of wet sand and obtains the optimal parameter combination for the Edinburgh elasto-plastic adhesion constitutive model. Variational autoencoder-based principal component ordering is employed by PartsNet to reduce the high-dimensional dynamic response and extract critical parameters along with their weights. …”
Get full text

About Methods for Classifying Hidden Language Concepts in Specialized Texts Involving Pseudoinverse, Clustering and Data Grouping by Iurii Krak, Anatoliy Kulias, Valentina Petrovych, Vladyslav Kuznetsov

“…The stability of the proposed method is investigated by using the perturbation of the original data by a variational autoencoder, test runs shown that sparse autocoder reduces the mean square error, but the separation band decreases, which affects the convergence of the classification algorithm. …”
Get full text

Cumulus cloud modeling from images based on VAE-GAN by Zili Zhang, Yunchi Cen, Fan Zhang, Xiaohui Liang

“…The method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial network. …”
Get full text

Multimodal Stereotactic Brain Tumor Segmentation Using 3D-Znet by Mohammad Ashraf Ottom, Hanif Abdul Rahman, Iyad M. Alazzam, Ivo D. Dinov

“…This study proposes an enhanced deep neural network approach, the 3D-Znet model, based on the variational autoencoder–autodecoder Znet method, for segmenting 3D MR (magnetic resonance) volumes. …”
Get full text

BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk. by Natalie R Davidson, Fan Zhang, Casey S Greene

“…BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. …”
Get full text

Addressing Missing Data Challenges in Geriatric Health Monitoring: A Study of Statistical and Machine Learning Imputation Methods by Gabriel-Vasilică Sasu, Bogdan-Iulian Ciubotaru, Nicolae Goga, Andrei Vasilățeanu

“…Imputation methods, including Expectation–Maximization (EM), matrix completion, Bayesian networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), Generative Adversarial Imputation Networks (GAINs), Variational Autoencoder (VAE), and GRU-D, were evaluated based on normalized Mean Squared Error (MSE), Mean Absolute Error (MAE), and R² metrics. …”
Get full text

Fault Diagnosis of Magnetically Controlled On-Column Circuit Breaker Based on Small Sample Condition by He Tian, Chao Liang, Wenpeng Ma, Tianchang Zhang

“…Initially, a Variational Autoencoder (VAE) is employed to extract the latent distribution of genuine samples, which are then integrated with the Auxiliary Classifier Generative Adversarial Network (ACGAN) generator to learn the characteristics of real data. …”
Get full text

NOISY IMAGE CLASSIFICATION USING HYBRID DEEP LEARNING METHODS by Sudipta Singha Roy, Mahtab Uddin Ahmed, Muhammad Akhand

“…In the denoising step, a variety of existing AEs, named denoising autoencoder (DAE), convolutional denoising autoencoder  (CDAE)  and  denoising  variational  autoencoder (DVAE) as well as two hybrid AEs (DAE-CDAE and DVAE- CDAE) were used. …”
Get full text

A deep learning generative model approach for image synthesis of plant leaves. by Alessandro Benfenati, Davide Bolzi, Paola Causin, Roberto Oberti

“…<h4>Methods</h4>Following an approach based on DL generative models, we introduce a Leaf-to-Leaf Translation (L2L) algorithm, able to produce collections of novel synthetic images in two steps: first, a residual variational autoencoder architecture is used to generate novel synthetic leaf skeletons geometry, starting from binarized skeletons obtained from real leaf images. …”
Get full text

A Deep Generative Model with Multiscale Features Enabled Industrial Internet of Things for Intelligent Fault Diagnosis of Bearings by He-xuan Hu, Yicheng Cai, Qiang Hu, Ye Zhang

“…Specifically, the DGMMF model uses 4 different variational autoencoder models to augment the bearing data and integrates features of different scales. …”
Get full text