Essays about: "VAE"

Showing result 1 - 5 of 45 essays containing the word VAE.

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

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

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

    Abstract : 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. READ MORE

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

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Kobe Moerman; [2023]
    Keywords : 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;

    Abstract : 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. READ MORE

  3. 3. Enhancing Long-Term Human Motion Forecasting using Quantization-based Modelling. : Integrating Attention and Correlation for 3D Motion Prediction

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Luis González Gudiño; [2023]
    Keywords : Human Motion Forecasting; Long-Term Prediction; VQ-VAE; Quantization; 3D Human Motion; CMU MoCap Dataset; Transformer; Mänsklig Rörelseprognos; Långsiktig Prognos; VQ-VAE; Kvantisering; 3D-mänsklig rörelse; CMU MoCap Dataset; Transformer;

    Abstract : This thesis focuses on addressing the limitations of existing human motion prediction models by extending the prediction horizon to very long-term forecasts. The objective is to develop a model that achieves one of the best stable prediction horizons in the field, providing accurate predictions without significant error increase over time. READ MORE

  4. 4. EVALUATING PERFORMANCE OF GENERATIVE MODELS FOR TIME SERIES SYNTHESIS

    University essay from Mälardalens universitet/Akademin för innovation, design och teknik

    Author : Muhammad Junaid Haris; [2023]
    Keywords : GAN; Generative Adversarial Network; VQ-VAE; Vector Quantized Variational AutoEncoder; AutoEncoder; VAE; Time Series; Synthesizing; Data Synthesis;

    Abstract : Motivated by successes in the image generation domain, this thesis presents a novel Hybrid VQ-VAE (H-VQ-VAE) approach for generating realistic synthetic time series data with categorical features. The primary motivation behind this work is to address the limitations of existing generative models in accurately capturing the underlying structure and patterns of time series data, especially when dealing with categorical features. READ MORE

  5. 5. Latent Data-Structures for Complex State Representation : A Steppingstone to Generating Synthetic 5G RAN data using Deep Learning

    University essay from Uppsala universitet/Högenergifysik

    Author : Jakob Häggström; [2023]
    Keywords : Data Science; Machine Learning; Generative models; Artificial Intelligence; 5GRAN;

    Abstract : The aim of this thesis is to investigate the feasibility of applying generative deep learning models on data related to 5G Radio Access Networks (5GRAN). Simulated data is used in order to develop the generative models, and this project serves as a proof of concept for further applications on real data. READ MORE