Essays about: "Semi- Supervised Learning"
Showing result 6 - 10 of 85 essays containing the words Semi- Supervised Learning.
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6. Semi-Supervised Head Detection for Low Resolution Images
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Object detection is a widely researched and applied field in computer vision. Deep learning models have successfully been used for object detection over the years. The performance of State of the art (SOTA) object detection deep learning models is dependent on the number of labeled images. READ MORE
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7. 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
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE
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8. 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
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE
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9. A study about Active Semi-Supervised Learning for Generative Models
University essay from Linköpings universitet/Institutionen för datavetenskapAbstract : 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. READ MORE
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10. Semi-Supervised Domain Adaptation for Semantic Segmentation with Consistency Regularization : A learning framework under scarce dense labels
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE
