Saliency Map Based Data Augmentation

Data augmentation is a commonly applied technique with two seemingly related advantages. With this method one can increase the size of the training set generating new samples and also increase the invariance of the network against the applied transformations. Unfortunately all images contain both re...

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Bibliographic Details
Main Authors: Al-Afandi, Jalal, Magyar, Balint, Horvath, Andras
Format: Conference Proceeding
Language:English
Subjects:
Network architecture
Neural networks
Pattern recognition
Task analysis
Training
Online Access:Request full text
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Description
Summary:Data augmentation is a commonly applied technique with two seemingly related advantages. With this method one can increase the size of the training set generating new samples and also increase the invariance of the network against the applied transformations. Unfortunately all images contain both relevant and irrelevant features for classification therefore this invariance has to be class specific. In this paper we will present a new method which uses saliency maps to restrict the invariance of neural networks to certain regions, providing higher test accuracy in classification tasks.
ISSN:2831-7475
DOI:10.1109/ICPR56361.2022.9956346