Advancing Capsule Networks: Addressing CNN Limitations for Hierarchical Feature Learning
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V1I3P101Keywords:
Capsule Networks, CNN, Dynamic Routing, Adaptive Capsule Size, Multi-Task Learning, Image Classification, Pose Invariance, Feature Learning, Computational Efficiency, Deep LearningAbstract
Convolutional Neural Networks (CNNs) have been the backbone of numerous breakthroughs in computer vision, but they are not without limitations. One of the primary drawbacks is their inability to effectively capture hierarchical relationships and spatial hierarchies in data, which are crucial for tasks such as object recognition and scene understanding. Capsule Networks (CapsNets) were introduced to address these limitations by encoding spatial hierarchies and part-whole relationships in a more structured manner. This paper explores the advancements in Capsule Networks, their theoretical foundations, and practical applications. We also compare CapsNets with traditional CNNs, highlighting the advantages and challenges of each. Finally, we propose new algorithms and techniques to enhance the performance of CapsNets, making them more robust and efficient for realworld applications
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References
[1] Sabour, S., Frosst, N., & Hinton, G. E. (2017). Dynamic routing between capsules. Advances in Neural Information Processing Systems, 30, 3856–3866.
[2] Hinton, G. E., Krizhevsky, A., & Wang, S. D. (2018). Matrix capsules with EM routing. International Conference on Learning Representations.
[3] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
[4] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
[5] LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
[6] Zhao, J., Gallo, O., Frosio, I., & Kautz, J. (2016). Loss functions for image restoration with neural networks. IEEE Transactions on Computational Imaging, 3(1), 47–57.
[7] Lin, M., Chen, Q., & Yan, S. (2013). Network in network. arXiv preprint arXiv:1312.4400.
[8] Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015). Highway networks. arXiv preprint arXiv:1505.00387.
[9] 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.
[10] Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. International Conference on Machine Learning, 448–456.
[11] Sabour, S., & Hinton, G. E. (2017). Capsules with inverted dot-product attention routing. arXiv preprint arXiv:1710.09829.
[12] Papernot, N., McDaniel, P., Wu, X., Jha, S., & Swami, A. (2016). Distillation as a defense to adversarial perturbations against deep neural networks. IEEE Symposium on Security and Privacy, 582–597.
[13] Geirhos, R., et al. (2018). Generalization in capsule networks. arXiv preprint arXiv:1811.03672.
[14] Kumar, A., & Chellappa, R. (2020). Capsule networks for object recognition. Computer Vision and Image Understanding, 201, 103061.
[15] Zhao, Y., et al. (2019). Capsule networks for NLP. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing.
[16] Wang, S., & Liu, Y. (2020). A survey on capsule networks. Journal of Artificial Intelligence Research, 69, 345–374.
[17] Zhang, S., Bengio, Y., & Hinton, G. (2019). Capsule networks for sequence modeling. Neural Computation, 31(5), 885–900.
[18] Qi, G. (2021). Hierarchical feature extraction in capsule networks. Pattern Recognition, 115, 107915.
[19] Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted Boltzmann machines. Proceedings of the 27th International Conference on Machine Learning, 807–814
