Sökning: "synthetic data generation"

Visar resultat 16 - 20 av 84 uppsatser innehållade orden synthetic data generation.

1. 16. Generating Synthetic Training Data with Stable Diffusion

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Rasmus Rynell; Oscar Melin; [2023]
   Nyckelord :AI; ML; Artificial Intelligence; Machine Learning; Stable Diffusion; ControlNet; Image classification; Image synthesis; Generative models; Generating images; Generating training data;

   Sammanfattning : The usage of image classification in various industries has grown significantly in recentyears. There are however challenges concerning the data used to train such models. Inmany cases the data used in training is often difficult and expensive to obtain. Furthermore,dealing with image data may come with additional problems such as privacy concerns. LÄS MER

3. 17. Generation of Synthetic White Blood Cell Images Using Denoising Diffusion

   Master-uppsats, Lunds universitet/Matematik LTH

   Författare :Louise Zettergren; Fanny Nilsson; [2023]
   Nyckelord :Mathematics and Statistics;

   Sammanfattning : CellaVision’s digital hematology systems are designed to analyze blood and pre-classify different types of blood cells. Some abnormal white blood cells are rare, which can cause imbalanced datasets. This can lead to a decrease in pre- classification performance and a need to carry out more time-consuming data gathering. LÄS MER

4. 18. Synthetic data generation for domain adaptation of a retriever-reader Question Answering system for the Telecom domain : Comparing dense embeddings with BM25 for Open Domain Question Answering

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Filip Döringer Kana; [2023]
   Nyckelord :Natural Language Processing; Transformers; Deep Learning; Question Answering; Data Generation; Språkteknologi; Transformers; Djupinlärning; Frågebesvaring; Datagenerering;

   Sammanfattning : Having computer systems capable of answering questions has been a goal within Natural Language Processing research for many years. Machine Learning systems have recently become increasingly proficient at this task with large language models obtaining state-of-the-art performance. LÄS MER

5. 19. MULTI-CLASS GRAMMATICAL ERROR DETECTION Data, Benchmarks and Evaluation Metrics for the First Shared Task on Swedish L2 Data

   Master-uppsats, Göteborgs universitet / Institutionen för filosofi, lingvistik och vetenskapsteori

   Författare :Judit Casademont Moner; [2022-06-20]
   Nyckelord :Grammatical Error Detection; L2 Swedish dataset; synthetic data; shared task;

   Sammanfattning : Grammatical Error Detection (GED) is a challenging NLP task that has not received a lot of research attention in the recent years, especially in the Swedish language. However, in the world we live in, where there are more L2 (second language) learners than there have ever been, educational resources for students such as tools for grammar checking are needed. LÄS MER

7. 20. Investigating Relations between Regularization and Weight Initialization in Artificial Neural Networks

   Kandidat-uppsats, Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisation

   Författare :Rasmus Sjöö; [2022]
   Nyckelord :Artificial Neural Networks; L1 Regularization; L2 Regularization; Loss Function; Maximum Likelihood; Regularization Strength Synthetic Data Generation; Weight Initialization; Physics and Astronomy;

   Sammanfattning : L2 regularization is a common method used to prevent overtraining in artificial neural networks. However, an issue with this method is that the regularization strength has to be properly adjusted for it to work as intended. This value is usually found by trial and error which can take some time, especially for larger networks. LÄS MER