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Showing result 1 - 5 of 19 essays matching the above criteria.
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1. Classification of Radar Emitters using Semi-Supervised Contrastive Learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Radar is a commonly used radio equipment in military and civilian settings for discovering and locating foreign objects. In a military context, pilots being discovered by radar could have fatal consequences. READ MORE
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2. 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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3. 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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4. Semi-Supervised Plant Leaf Detection and Stress Recognition
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : 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. READ MORE
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5. Deep Ensembles for Self-Training in NLP
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : With the development of deep learning methods the requirement of having access to large amounts of data has increased. In this study, we have looked at methods for leveraging unlabeled data while only having access to small amounts of labeled data, which is common in real-world scenarios. READ MORE
