What are the limitations of CNNs?

While Convolutional Neural Networks (CNNs) have proven to be highly effective in various computer vision tasks, they do have certain limitations. Firstly, CNNs require a large amount of labeled training data to achieve high performance, which can be challenging and time-consuming to obtain for certain domains. Additionally, CNNs lack the ability to generalize well outside the domain they have been trained on, making them less suitable for transfer learning. CNNs are also computationally intensive, requiring significant computational resources for both training and inference, making it difficult to deploy them on low-power or resource-constrained devices. Finally, CNNs may struggle with handling occlusion or partially visible objects and may fail to recognize them correctly. Overall, while CNNs have revolutionized computer vision, it is important to acknowledge and address these limitations in order to continue advancing the field.

This mind map was published on 20 August 2023 and has been viewed 59 times.