Effects of Transfer Learning on Data Augmentation with Generative Adversarial Networks

Effects of Transfer Learning on Data Augmentation with Generative Adversarial Networks

Data augmentation is a technique that acquires more training data by augmenting available samples, where the training data is used to fit model parameters. Data augmentation is utilized due to a shortage of training data in certain domains and to reduce overfitting. Augmenting a training dataset for...

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Bibliographic Details
Main Authors: Berglöf, Olle, Jacobs, Adam
Format: Others
Language:English
Published: KTH, Skolan för elektroteknik och datavetenskap (EECS) 2019
Subjects:
data augmentation
generative adversarial networks
GAN
image classification
transfer learning
image generator
generating training data
machine learning
Computer and Information Sciences
Data- och informationsvetenskap
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259485

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http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259485

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