Sökning: "Generaliseringsförmåga"
Visar resultat 1 - 5 av 14 uppsatser innehållade ordet Generaliseringsförmåga.
1. Exploring the Depth-Performance Trade-Off : Applying Torch Pruning to YOLOv8 Models for Semantic Segmentation Tasks
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : In order to comprehend the environments from different aspects, a large variety of computer vision methods are developed to detect objects, classify objects or even segment them semantically. Semantic segmentation is growing in significance due to its broad applications in fields such as robotics, environmental understanding for virtual or augmented reality, and autonomous driving. LÄS MER
2. Designprocessen och maskininlärning: Framtiden för användarcentrerad design
Kandidat-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskapSammanfattning : Artificiell intelligens (AI) och i synnerhet maskininlärning (ML) har inom UX-design visat potential att förbättra designprocessen genom att exempelvis identifiera användargrupper från stora datamängder, effektivisera idégenerering och automatisera repetitiva uppgifter. Det råder dock oenighet kring hur tekniken kan integreras i designprocessen. LÄS MER
3. Heart rate estimation from wrist-PPG signals in activity by deep learning methods
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : In the context of health improving, the measurement of vital parameters such as heart rate (HR) can provide solutions for health monitoring, prevention and screening for certain chronic diseases. Among the different technologies for HR measuring, photoplethysmography (PPG) technique embedded in smart watches is the most commonly used in the field of consumer electronics since it is comfortable and does not require any user intervention. LÄS MER
4. Analyzing How Blended Emotions are Expressed using Machine Learning Methods
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Blended emotion is a classification of emotional experiences that involve the combination of multiple emotions. Research on the expression of blended emotions allows researchers to understand how different emotions interact and coexist in an individual’s emotional experience. LÄS MER
5. Semi-supervised anomaly detection in mask writer servo logs : An investigation of semi-supervised deep learning approaches for anomaly detection in servo logs of photomask writers
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Semi-supervised anomaly detection is the setting, where in addition to a set of nominal samples, predominantly normal, a small set of labeled anomalies is available at training. In contrast to supervised defect classification, these methods do not learn the anomaly class directly and should have better generalization capability as new kinds of anomalies are introduced at test time. LÄS MER
