Essays about: "Variational Autoencoder VAE"
Showing result 1 - 5 of 28 essays containing the words Variational Autoencoder VAE.
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1. Modulating Depth Map Features to Estimate 3D Human Pose via Multi-Task Variational Autoencoders
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)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
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2. 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)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
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3. EVALUATING PERFORMANCE OF GENERATIVE MODELS FOR TIME SERIES SYNTHESIS
University essay from Mälardalens universitet/Akademin för innovation, design och teknikAbstract : 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
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4. Latent Data-Structures for Complex State Representation : A Steppingstone to Generating Synthetic 5G RAN data using Deep Learning
University essay from Uppsala universitet/HögenergifysikAbstract : 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
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5. MmWave Radar-based Deep Learning Collision Prediction
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Autonomous drone navigation in classical approaches typically involves constructing a map representation and employing path planning and collision checking algorithms within that map. Recently, novel deep learning techniques combined with depth camera observations have emerged as alternative approaches capable of achieving comparable collision-free performance. READ MORE
