Essays about: "Synthetic Data Quality Evaluation"

Showing result 1 - 5 of 15 essays containing the words Synthetic Data Quality Evaluation.

1. 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)

   Author : Alexander Florean; [2024]
   Keywords : Synthetic Data; Machine Learning; Population Fidelity Measures; Utility Metrics; Synthetic Data Quality Evaluation; Classification Algorithms; Utility Estimation; Data Privacy; Artificial Intelligence; Experiment Framework; Model Performance Assessment; Syntetisk Data; Maskininlärning; Population Fidelity Mätvärden; Användbarhetsmätvärden; Kvalitetsutvärdering av Syntetisk Data; Klassificeringsalgoritmer; Användbarhetsutvärdering; Dataintegritet; Artificiell Intelligens; AI; Experiment Ramverk; Utvärdering av Modellprestanda;

   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

3. 2. Analyzing the Influence of Synthetic andAugmented Data on Segmentation Model

   University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknik

   Author : Alex Peschel; [2023]
   Keywords : Artificial Intelligence; Microorganisms; Segmentation; Synthesizing; Augmentation;

   Abstract : 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

4. 3. Messing With The Gap: On The Modality Gap Phenomenon In Multimodal Contrastive Representation Learning

   University essay from Uppsala universitet/Industriell teknik

   Author : Mohammad Al-Jaff; [2023]
   Keywords : Multimodal machine learning; Representation learning; Self-supervised learning; contrastive learning; computer vision; computational biology; bioinformatics;

   Abstract : 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

5. 4. A comparative analysis of database sanitization techniques for privacy-preserving association rule mining

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Charlie Mårtensson; [2023]
   Keywords : Association rule hiding; privacy-preserving data mining; evolutionary algorithms; performance evaluation; Associationsregeldöljning; sekretessbevarande datautvinning; evolutionära algoritmer; prestandaevaluering;

   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

6. 5. Impact of MR training data on the quality of synthetic CT generation

   University essay from Umeå universitet/Institutionen för fysik

   Author : Gustav Jönsson; [2022]
   Keywords : Generative adversarial network; Machine learning; Radiotherapy; Synthetic CT; MR; CT;

   Abstract : 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