An Efficient Convolutional Neural Network Framework for  Image Classification with Limited Training Data

Shabana Nargis Rasool

An Efficient Convolutional Neural Network Framework for Image Classification with Limited Training Data

Authors

Shabana Nargis Rasool

Keywords:

Deep Learning, Convolutional Neural Networks, Image Classification, Data Augmentation, Limited Data, Regularization, Batch Normalization, Dropout, Computer Vision

Abstract

he remarkable success of deep learning, particularly Convolutional Neural Networks(CNNs), has significantly advanced image classification tasks in com-puter vision. However, the performance of deep models is often constrained by the availabilityof large-scale annotated datasets, which are not always

References

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS) (2012)

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

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Published

2023-01-20

How to Cite

Shabana Nargis Rasool. (2023). An Efficient Convolutional Neural Network Framework for Image Classification with Limited Training Data. Journal of Computational Analysis and Applications (JoCAAA), 31(1), 1497–1511. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5520