Original title: Techniques For Avoiding Model Overfitting On Small Dataset
Authors: Kratochvila, Lukas
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: Building a deep learning model based on small dataset is difficult, even impossible. Toavoiding overfitting, we must constrain model, which we train. Techniques as data augmentation,regularization or data normalization could be crucial. We have created a benchmark with a simpleCNN image classifier in order to find the best techniques. As a result, we compare different types ofdata augmentation and weights regularization and data normalization on a small dataset.
Keywords: Batch Normalization; Data Augmentation; Data Normalization; Dataset size; Deep Learning; ImageClassification; Overfitting; Regularization
Host item entry: Proceedings I of the 27st Conference STUDENT EEICT 2021: General papers, ISBN 978-80-214-5942-7

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/200799

Permalink: http://www.nusl.cz/ntk/nusl-447844


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Conference materials > Papers