TECHNIQUES FOR AVOIDING MODEL OVERFITTING ON SMALL DATASET

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU140621" target="_blank" >RIV/00216305:26220/21:PU140621 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
TECHNIQUES FOR AVOIDING MODEL OVERFITTING ON SMALL DATASET
Original language description
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.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings II of the 27th Conference STUDENT EEICT 2021
ISBN
978-80-214-5868-0
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
451-456
Publisher name
Vysoké učené Technické, Fakulta elektrotechniky a komunikačních technologií
Place of publication
Brno
Event location
Brno
Event date
Apr 27, 2021
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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