Essays about: "semi-övervakad"
Showing result 1 - 5 of 19 essays containing the word semi-övervakad.
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1. Applicability of Detection Transformers in Resource-Constrained Environments : Investigating Detection Transformer Performance Under Computational Limitations and Scarcity of Annotated Data
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Object detection is a fundamental task in computer vision, with significant applications in various domains. However, the reliance on large-scale annotated data and computational resource demands poses challenges in practical implementation. READ MORE
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2. 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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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
