Sökning: "Variational Autoencoders VAEs"

Visar resultat 1 - 5 av 8 uppsatser innehållade orden Variational Autoencoders VAEs.

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. Prediction of Persistence to Treatment for Patients with Rheumatoid Arthritis using Deep Learning

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

   Författare :Serkan Arda Yilal; [2023]
   Nyckelord :Variational Autoencoders; Rheumatoid Arthritis; Precision Medicine; Treatment Prediction; Deep Learning; Supervised Learning; Rheumatoid Artrit; Precisionsmedicin; Behandlingsförutsägelse; Djupinlärning; Övervakat lärande;

   Sammanfattning : Rheumatoid Arthritis is an inflammatory joint disease that is one of the most common autoimmune diseases in the world. The treatment usually starts with a first-line treatment called Methotrexate, but it is often insufficient. One of the most common second-line treatments is Tumor Necrosis Factor inhibitors (TNFi). LÄS MER

7. 5. Sign of the Times : Unmasking Deep Learning for Time Series Anomaly Detection

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

   Författare :Daniel Richards Ravi Arputharaj; [2023]
   Nyckelord :Anomaly detection; multivariate time series data; deep learning models; model complexity; resource-constrained systems; Variational Autoencoders VAEs ; Convolutional Variational Autoencoders; evaluation metrics in time series; Anomalidetektering; Multivariata tidsseriedata; Djupinlärningsmodeller; Modellkomplexitet; Resursbegränsade system; Variational Autoencoders VAEs ; Konvolutionella Variational Autoencoders; Utvärderingsmått inom tidsserier;

   Sammanfattning : Time series anomaly detection has been a longstanding area of research with applications across various domains. In recent years, there has been a surge of interest in applying deep learning models to this problem domain. LÄS MER