Sökning: "Variationsautokodare"

Hittade 4 uppsatser innehållade ordet Variationsautokodare.

1. 1. 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

3. 2. Attribute Embedding for Variational Auto-Encoders : Regularization derived from triplet loss

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

   Författare :Anton E. L. Dahlin; [2022]
   Nyckelord :Variational Auto-Encoder; Triplet Loss; Contrastive Loss; Generative Models; Metric Learning; Latent Space; Attribute Manipulation; Variationsautokodare; Triplettförlust; Kontrastiv Förlust; Generativa Modeller; Metrisk Inlärning; Latent Utrymme; Attributmanipulation;

   Sammanfattning : Techniques for imposing a structure on the latent space of neural networks have seen much development in recent years. Clustering techniques used for classification have been used to great success, and with this work we hope to bridge the gap between contrastive losses and Generative models. LÄS MER

4. 3. A deep learning based anomaly detection pipeline for battery fleets

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

   Författare :Nabakumar Singh Khongbantabam; [2021]
   Nyckelord :Forklift batteries; Battery sensors; Data pipeline; Predictive maintenance; Anomaly detection; Deep learning; Battery failure prediction; Time-series; Variational autoencoder; Long short-term memory; LSTM; Gated recurrent unit; GRU; Isolation nearest neighbor; iNNE; Isolation forest; iForest; kth nearest neighbor; kNN.; Gaffeltruckbatterier; Batterisensorer; Datapipeline; Prediktivt underhåll; Avvikelsedetektering; Deep learning; Batterifelsprediktion; Tidsserier; Variationsautokodare; Långt korttidsminne; LSTM; Gated recurrent unit; GRU; Isolation närmaste granne; iNNE; Isolation skog; iForest; kth närmaste granne; kNN.;

   Sammanfattning : This thesis proposes a deep learning anomaly detection pipeline to detect possible anomalies during the operation of a fleet of batteries and presents its development and evaluation. The pipeline employs sensors that connect to each battery in the fleet to remotely collect real-time measurements of their operating characteristics, such as voltage, current, and temperature. LÄS MER

5. 4. Automatic Question Paraphrasing in Swedish with Deep Generative Models

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

   Författare :Niklas Lindqvist; [2021]
   Nyckelord :Paraphrase Generation; Variational Autoencoder; Generative Adversarial Networks; Natural Language Generation; Deep Learning; Word Embeddings; Parafrasgenerering; Variational Autoencoder; generativa adversariala nätverk; naturlig språkgenerering; djupinlärning; ordinbäddning;

   Sammanfattning : Paraphrase generation refers to the task of automatically generating a paraphrase given an input sentence or text. Paraphrase generation is a fundamental yet challenging natural language processing (NLP) task and is utilized in a variety of applications such as question answering, information retrieval, conversational systems etc. LÄS MER