Which deep learning architecture uses convolutional layers to learn spatial hierarchies of features from grid-like data?

The choice of a convolutional neural network (CNN) is appropriate in this context because CNNs are specifically designed to process grid-like data, such as images. They utilize convolutional layers to automatically and adaptively learn spatial hierarchies of features. This means that they can identify patterns at various scales—starting from simple edges and textures to more complex shapes and objects—by applying convolutional operations across the input data.

The architecture of CNNs allows them to effectively capture local dependencies and reduce the number of parameters compared to fully connected networks. This is particularly advantageous when dealing with high-dimensional inputs like images, where the spatial relationship between pixels is crucial for understanding content.

In contrast, the other architectures mentioned have different structural purposes:

Recurrent neural networks (RNNs) are tailored for sequential data, such as time series or natural language, where temporal dependencies are key. Feedforward neural networks emphasize simpler structures where data flows in one direction without cycles or feedback, making them less effective for image processing tasks that require understanding spatial hierarchies. Lastly, generative adversarial networks (GANs), while involving CNNs in many implementations, are primarily focused on generating new data that mimics a training set, rather than directly learning features from grid-like