Essays about: "Semi-övervakad inlärning"
Showing result 1 - 5 of 13 essays containing the words Semi-övervakad inlärning.
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1. 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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2. 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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3. 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
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4. LaMOSNet: Latent Mean-Opinion-Score Network for Non-intrusive Speech Quality Assessment : Deep Neural Network for MOS Prediction
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Objective non-intrusive speech quality assessment aimed to emulate and correlate with human judgement has received more attention over the years. It is a difficult problem due to three reasons: data scarcity, noisy human judgement, and a potential uneven distribution of bias of mean opinion scores (MOS). READ MORE
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5. Style Transfer Paraphrasing for Consistency Training in Sentiment Classification
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Text data is easy to retrieve but often expensive to classify, which is why labeled textual data is a resource often lacking in quantity. However, the use of labeled data is crucial in supervised tasks such as text classification, but semi-supervised learning algorithms have shown that the use of unlabeled data during training has the potential to improve model performance, even in comparison to a fully supervised setting. READ MORE
