What kind of layer cannot be modified extensively?

A "frozen layer" in a neural network refers to a layer that cannot be modified extensively during training. This typically occurs when the weights of the layer are set to be non-trainable, often to retain learned features from pre-trained models. Frozen layers are commonly used in transfer learning scenariOS to leverage existing knowledge while adapting only certain parts of the network to new tasks.