convolutional-layer

Convolutional layers are a core component of convolutional neural networks (CNNs) and are designed to automatically and adaptively learn spatial hierarchies of features from input images. Each layer applies a series of filters to the input to produce feature maps that capture spatial relationships. A common misconception is that increasing the number of layers always leads to better performance; however, deeper networks can also lead to overfitting if not managed properly.