Essays about: "Synthetic Data Quality Evaluation"
Showing result 1 - 5 of 15 essays containing the words Synthetic Data Quality Evaluation.
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1. Measuring the Utility of Synthetic Data : An Empirical Evaluation of Population Fidelity Measures as Indicators of Synthetic Data Utility in Classification Tasks
University essay from Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)Abstract : In the era of data-driven decision-making and innovation, synthetic data serves as a promising tool that bridges the need for vast datasets in machine learning (ML) and the imperative necessity of data privacy. By simulating real-world data while preserving privacy, synthetic data generators have become more prevalent instruments in AI and ML development. READ MORE
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2. Analyzing the Influence of Synthetic andAugmented Data on Segmentation Model
University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknikAbstract : The field of Artificial Intelligence (AI) has experienced unprecedented growth in recent years, thanks to the numerous applications related to speech recognition, natural language processing, and computer vision. However, one of the challenges facing AI is the requirement for large amounts of energy, time, and data to be effective and accurate. READ MORE
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3. Messing With The Gap: On The Modality Gap Phenomenon In Multimodal Contrastive Representation Learning
University essay from Uppsala universitet/Industriell teknikAbstract : In machine learning, a sub-field of computer science, a two-tower architecture model is a specialised type of neural network model that encodes paired data from different modalities (like text and images, sound and video, or proteomics and gene expression profiles) into a shared latent representation space. However, when training these models using a specific contrastive loss function, known as the multimodalinfoNCE loss, seems to often lead to a unique geometric phenomenon known as the modality gap. READ MORE
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4. A comparative analysis of database sanitization techniques for privacy-preserving association rule mining
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Association rule hiding (ARH) is the process of modifying a transaction database to prevent sensitive patterns (association rules) from discovery by data miners. An optimal ARH technique successfully hides all sensitive patterns while leaving all nonsensitive patterns public. READ MORE
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5. Impact of MR training data on the quality of synthetic CT generation
University essay from Umeå universitet/Institutionen för fysikAbstract : Both computed tomography (CT) and magnetic resonance imaging (MRI) have a purpose for radiotherapy. But having two imaging sessions brings uncertainty which makes it beneficial to create synthetic CT (sCT) images from MR images. In this work a Generative Adversarial Network (GAN) was designed and implemented for sCT generation. READ MORE