Sökning: "Semi-supervised learning"

Visar resultat 6 - 10 av 78 uppsatser innehållade orden Semi-supervised learning.

1. 6. 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)

   Författare :Toomas Liiv; [2023]
   Nyckelord :anomaly detection; semi-supervision; HSC; DeepSAD; photomasks; anomalidetektion; semi-övervakad; HSC; DeepSAD; fotomasker;

   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

3. 7. Anomaly detection for prediction of failures in manufacturing environments : Machine learning based semi-supervised anomaly detection for multivariate time series to predict failures in a CNC-machine

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

   Författare :Felix Boltshauser; [2023]
   Nyckelord :Machine learning; Anomaly Detection; DeepAnT; ROCKET; OCSVM; manufacturing; predictive maintenance; Maskin inlärning; Anomali Detektion; DeepAnT; ROCKET; OCSVM; tillverkning; prediktivt underhåll;

   Sammanfattning : For manufacturing enterprises, the potential of collecting large amounts of data from production processes has enabled the usage of machine learning for prediction-based monitoring and maintenance of machines. Yet common maintenance strategies still include reactive handling of machine failures or schedule-based maintenance conducted by experienced personnel. LÄS MER

4. 8. A study about Active Semi-Supervised Learning for Generative Models

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Elisio Fernandes de Almeida Quintino; [2023]
   Nyckelord :Semi-Supervised Learning; Active Learning; Generative Models; Mixture Models; Semi-Övervakad Inlärning; Aktiv Inlärning; Generativa Modeller; Mixturmodeller;

   Sammanfattning : In many relevant scenarios, there is an imbalance between abundant unlabeled data and scarce labeled data to train predictive models. Semi-Supervised Learning and Active Learning are two distinct approaches to deal with this issue. LÄS MER

5. 9. Semi-Supervised Domain Adaptation for Semantic Segmentation with Consistency Regularization : A learning framework under scarce dense labels

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

   Författare :Daniel Morales Brotons; [2023]
   Nyckelord :Domain Adaptation; Semi-Supervised Learning; Semi-Supervised Domain Adaptation; Semantic Segmentation; Consistency Regularization; Domain Adaptation; Semi-Supervised Learning; Semi-Supervised Domain Adaptation; Semantisk Segmentering; Konsistensregularisering;

   Sammanfattning : Learning from unlabeled data is a topic of critical significance in machine learning, as the large datasets required to train ever-growing models are costly and impractical to annotate. Semi-Supervised Learning (SSL) methods aim to learn from a few labels and a large unlabeled dataset. LÄS MER

7. 10. Semi-Supervised Plant Leaf Detection and Stress Recognition

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

   Författare :Márk Antal Csizmadia; [2022]
   Nyckelord :Deep Learning; Object Detection; Semi-Supervised Learning; Semi-Supervised Object Detection; Computer Vision; Djupinlärning; Objektdetektering; Semi-övervakad inlärning; Semi-övervakad objektdetektering; datorseende;

   Sammanfattning : One of the main limitations of training deep learning-based object detection models is the availability of large amounts of data annotations. When annotations are scarce, semi-supervised learning provides frameworks to improve object detection performance by utilising unlabelled data. LÄS MER