Sökning: "Adversarial Adaptation"

Visar resultat 1 - 5 av 14 uppsatser innehållade orden Adversarial Adaptation.

1. 1. Domain Adaptation for Multi-Contrast Image Segmentation in Cardiac Magnetic Resonance Imaging

   Master-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)

   Författare :Thomas Proudhon; [2023]
   Nyckelord :Cardiac Magnetic Resonance Imaging; Deep Learning; Domain Adaptation; Unsupervised Segmentation; Image-to-image Translation;

   Sammanfattning : Accurate segmentation of the ventricles and myocardium on Cardiac Magnetic Resonance (CMR) images is crucial to assess the functioning of the heart or to diagnose patients suffering from myocardial infarction. However, the domain shift existing between the multiple sequences of CMR data prevents a deep learning model trained on a specific contrast to be used on a different sequence. LÄS MER

3. 2. Towards Building Privacy-Preserving Language Models: Challenges and Insights in Adapting PrivGAN for Generation of Synthetic Clinical Text

   Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

   Författare :Atena Nazem; [2023]
   Nyckelord :Generative Adversarial Networks; privacy-preserving language models; clinical text data; reinforcement learning; synthetic data;

   Sammanfattning : The growing development of artificial intelligence (AI), particularly neural networks, is transforming applications of AI in healthcare, yet it raises significant privacy concerns due to potential data leakage. As neural networks memorise training data, they may inadvertently expose sensitive clinical data to privacy breaches, which can engender serious repercussions like identity theft, fraud, and harmful medical errors. LÄS MER

4. 3. Domain Adaptation Of Front View Synthetic Point Clouds Using GANs For Autonomous Driving

   Master-uppsats, KTH/Väg- och spårfordon samt konceptuell fordonsdesign

   Författare :Friedemann Kleinsteuber; [2023]
   Nyckelord :LiDAR; Domain Adaptation; GAN; CycleGAN; Simulation; LiDAR; Domänadaption; GAN; CycleGAN; Simulation;

   Sammanfattning : The perception of the environment is one of the main enablers of Autonomous Driving and is driven by Cameras, RADAR, and LiDAR sensors. Deep Learning algorithms used in perception need a vast amount of labeled, high-quality data which is costly to obtain for LiDAR sensors. LÄS MER

5. 4. Unsupervised Domain Adaptation for Regressive Annotation : Using Domain-Adversarial Training on Eye Image Data for Pupil Detection

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

   Författare :Erik Zetterström; [2023]
   Nyckelord :Neural networks; Deep learning; Convolutional neural networks; Transfer learning; Domain adaptation; Unsupervised training; Adversarial training; Keypoint detection; Regression; Neurala nätverk; Djupinlärning; Faltningsnätverk; Överförningsinlärning; Domänadaptering; Oövervakad inlärning; Motstående träning; Nyckelpunktsdetektion; Regression;

   Sammanfattning : Machine learning has seen a rapid progress the last couple of decades, with more and more powerful neural network models continuously being presented. These neural networks require large amounts of data to train them. LÄS MER

7. 5. Unsupervised Domain Adaptation for 3D Object Detection Using Adversarial Adaptation : Learning Transferable LiDAR Features for a Delivery Robot

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

   Författare :Mattias Hansson; [2023]
   Nyckelord :Unsupervised Domain Adaptation; 3D Object Detection; Mobile Robotics; Adversarial Adaptation; Computer Vision; Oövervakad Domänanpassning; 3D Objektigenkänning; Mobila Robotar; Motspelaranpassning; Datorseende;

   Sammanfattning : 3D object detection is the task of detecting the full 3D pose of objects relative to an autonomous platform. It is an important perception system that can be used to plan actions according to the behavior of other dynamic objects in an environment. LÄS MER