Understanding CNNs with CNN Explainer: A High Level Description of Convolutional Neural Networks Architecture
CNNs are specialized classifiers designed for image recognition tasks, leveraging the concept of neural networks to recognize patterns within data. The narrative explains the structure of CNNs, detailing essential elements such as tensors, neurons, layers, and kernel weights, which collectively contribute to the network’s ability to learn and classify images effectively.
It highlights the significance of convolutional layers in CNN architecture, emphasizing their role in feature extraction from input images. Additionally, the discussion on hyperparameters like padding, kernel size, and stride elucidates their influence on the network’s performance and efficiency, providing insights into architectural design considerations.
Additionally, the explanation explores into the activation functions employed in CNNs, particularly focusing on Rectified Linear Units (ReLU) and Softmax functions. It exposes the importance of non-linearity in CNNs and the role of ReLU in introducing non-linear decision boundaries, crucial for enhancing model accuracy.
The Softmax function’s utility in scaling model outputs into probabilities is also underscored, facilitating classification by ensuring output probabilities sum to 1. Besides, the narrative explores pooling layers and flatten layers, elucidating their functions in reducing spatial dimensions and preparing feature maps for classification, respectively.
Overall, the explanation offers a comprehensive understanding of CNNs, their architectural components, and operational mechanisms, providing valuable insights into their applicability in image recognition tasks.
The CNN Explainer can be found in this link.
References
Wang, J., Turko, R., Shaikh, O., Park, H., Das, N., Hohman, F., Kahng, M. and Chau, P. (2020a). CNN Explainer. [online] poloclub.github.io. Available at: https://poloclub.github.io/cnn-explainer/ [Accessed 31 Jan. 2024].
Wang, Z.J., Turko, R., Shaikh, O., Park, H., Das, N., Hohman, F., Kahng, M. and Chau, D.H. (2020b). CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization. arXiv:2004.15004 [cs]. [online] Available at: https://arxiv.org/abs/2004.15004 [Accessed 31 Jan. 2024].