Use of Neural Networks for Image Classification
Keywords:
Neural networks, image classification, deep learning, convolutional neural networks, computer vision.Abstract
Neural networks have become the foundation of modern image classification systems, enabling significant advances in computer vision. Their ability to learn hierarchical representations directly from raw pixel data allows them to outperform traditional machine learning approaches. This article provides an overview of the principles, architectures, training techniques, and challenges associated with applying neural networks to image classification tasks. We analyze commonly used models, discuss optimization strategies, and highlight trends shaping the future of neural image classification.
References
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
Szegedy, C., Liu, W., Jia, Y., et al. (2015). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–9.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. International Conference on Learning Representations (ICLR).
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Russakovsky, O., Deng, J., Su, H., et al. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115, 211–252.
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(60).
Hendrycks, D., & Dietterich, T. (2019). Benchmarking neural network robustness to common corruptions and perturbations. International Conference on Learning Representations (ICLR).
Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, 6105–6114.
Howard, A. G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861.
Lin, M., Chen, Q., & Yan, S. (2014). Network in network. arXiv:1312.4400.
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495.
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27–48.
