Which of the following layers is NOT typically used in a CNN?

In the context of Convolutional Neural Networks (CNNs), the architecture typically includes several key layers to process image data effectively. A convolutional layer is crucial for feature extraction, applying filters to the input data to capture essential patterns such as edges and textures. The pooling layer follows to reduce the spatial dimensions of the feature maps, which helps decrease computational load and control overfitting. Fully connected layers, particularly at the end of the network, are used to make predictions based on the features extracted by the previous layers.

However, the recurrent layer is not used in CNNs as it is characteristic of Recurrent Neural Networks (RNNs), which are designed to handle sequential data like time series or natural language. This distinction is significant, as CNNs are optimized for tasks involving spatial data, such as image classification, while RNNs are tailored to sequential input, where the order of data points is crucial. Thus, the recurrent layer does not align with the typical structure of a CNN and is the layer that does not belong in this architecture.