Essays about: "Supervised Contrastive Learning"
Showing result 1 - 5 of 18 essays containing the words Supervised Contrastive Learning.
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1. Information Extraction for Test Identification in Repair Reports in the Automotive Domain
University essay from Uppsala universitet/Institutionen för lingvistik och filologiAbstract : The knowledge of tests conducted on a problematic vehicle is essential for enhancing the efficiency of mechanics. Therefore, identifying the tests performed in each repair case is of utmost importance. This thesis explores techniques for extracting data from unstructured repair reports to identify component tests. READ MORE
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2. Understanding the Robustnessof Self Supervised Representations
University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknikAbstract : This work investigates the robustness of learned representations of self-supervised learn-ing approaches, focusing on distribution shifts in computer vision. Joint embedding architecture and method-based self-supervised learning approaches have shown advancesin learning representations in a label-free manner and efficient knowledge transfer towardreducing human annotation needs. READ MORE
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3. 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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4. Anomaly Detection with Machine Learning using CLIP in a Video Surveillance Context
University essay from Linköpings universitet/DatorseendeAbstract : This thesis explores the application of Contrastive Language-Image Pre-Training (CLIP), a vision-language model, in an automated video surveillance system for anomaly detection. The ability of CLIP to perform zero-shot learning, coupled with its robustness against minor image alterations due to its lack of reliance on pixel-level image analysis, makes it a suitable candidate for this application. READ MORE
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5. 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
