Sökning: "Variational Autoencoders"

Visar resultat 1 - 5 av 39 uppsatser innehållade orden Variational Autoencoders.

1. 1. Variational AutoEncoders and Differential Privacy : balancing data synthesis and privacy constraints

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Baptiste Bremond; [2024]
   Nyckelord :TVAE; Differential privacy; Tabular data; Synthetic data; DP-SGD; TVAE; differentiell integritet; tabelldata; syntetiska data; DP-SGD;

   Sammanfattning : This thesis investigates the effectiveness of Tabular Variational Auto Encoders (TVAEs) in generating high-quality synthetic tabular data and assesses their compliance with differential privacy principles. The study shows that while TVAEs are better than VAEs at generating synthetic data that faithfully reproduces the distribution of real data as measured by the Synthetic Data Vault (SDV) metrics, the latter does not guarantee that the synthetic data is up to the task in practical industrial applications. LÄS MER

3. 2. Modulating Depth Map Features to Estimate 3D Human Pose via Multi-Task Variational Autoencoders

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Kobe Moerman; [2023]
   Nyckelord :3D pose estimation; Joint landmarks; Variational autoencoder; Multi-task model; Loss discrimination; Latent-space modulation; Depth map; 3D-positionsuppskattning; Gemensamma landmärken; Variationell autoencoder; Multitask-modell; Förlustdiskriminering; Latent-space-modulering; Djupkarta;

   Sammanfattning : Human pose estimation (HPE) constitutes a fundamental problem within the domain of computer vision, finding applications in diverse fields like motion analysis and human-computer interaction. This paper introduces innovative methodologies aimed at enhancing the accuracy and robustness of 3D joint estimation. LÄS MER

4. 3. Knowledge distillation for anomaly detection

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Nils Gustav Erik Pettersson; [2023]
   Nyckelord :;

   Sammanfattning : The implementation of systems and methodologies for time series anomaly detection holds the potential of providing timely detection of faults and issues in a wide variety of technical systems. Ideally, these systems are able to identify deviations from the normal behavior of systems even before any problems manifest, thus enabling proactive maintenance. LÄS MER

5. 4. Fault Detection and Diagnosis for Automotive Camera using Unsupervised Learning

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Ziyou Li; [2023]
   Nyckelord :Unsupervised Learning; Autoencoders; Image Clustering; Fault Detection and Diagnosis; Morphological Operations; Hardware-in-Loop; Advanced DriverAssistance System; Oövervakad inlärning; Autoencoders; Bildklustering; Felfindning och Diagnostik; Morfologiska Operationer; Hardware-in-Loop; Avancerade Förarassistanssystem;

   Sammanfattning : This thesis aims to investigate a fault detection and diagnosis system for automotive cameras using unsupervised learning. 1) Can a front-looking wide-angle camera image dataset be created using Hardware-in-Loop (HIL) simulations? 2) Can an Adversarial Autoencoder (AAE) based unsupervised camera fault detection and diagnosis method be crafted for SPA2 Vehicle Control Unit (VCU) using an image dataset created using Hardware-inLoop? 3) Does using AAE surpass the performance of using Variational Autoencoder (VAE) for the unsupervised automotive camera fault diagnosis model? In the field of camera fault studies, automotive cameras stand out for its complex operational context, particularly in Advanced Driver-Assistance Systems (ADAS) applications. LÄS MER

7. 5. Deep convolution neural network for attention decoding in multi-channel EEG with conditional variational autoencoder for data augmentation

   Uppsats för yrkesexamina på avancerad nivå, Lunds universitet/Institutionen för reglerteknik

   Författare :M Asjid Tanveer; [2023]
   Nyckelord :Technology and Engineering;

   Sammanfattning : Objectives: This project aims to develop a deep learning-based attention decoding system that can distinguish between noise and speech in noise and also identify the direction of attended speech from the brain data recorded with electroencephalography (EEG) instruments. Two deep convolutional neural network (DCNN) models will be designed: (1) one DCNN model capable of classifying incoming segments of sound as speech or speech in background noise, and (2) one DCNN model identifying the direction (left vs. LÄS MER