Convolutional layers in a CNN are responsible for learning hierarchical representations of the input data.Group of answer choicesTrueFalse

Verification of the Statement

1. Understanding Convolutional Layers: Convolutional layers in Convolutional Neural Networks (CNNs) apply convolution operations to the input data. This involves sliding a filter or kernel over the input image or feature map to learn local features.

2. Hierarchical Representations: One of the main features of CNNs is their ability to learn hierarchical representations. In the initial layers, the network often learns to identify simple features like edges and textures. As you go deeper into the network, it begins to recognize more complex patterns, such as shapes and various objects.

3. Role of Convolutional Layers: The statement asserts that convolutional layers are responsible for learning these hierarchical representations. This is accurate; convolutional layers not only extract features but also progressively build upon these features layer by layer.

Final Answer

True. Convolutional layers in a CNN are indeed responsible for learning hierarchical representations of the input data.