Layer Learning Dynamics

Layers in a neural network can learn individually, but synchronization is key for effective learning. When a layer falls behind, it may rely on outdated representations, leading to suboptimal performance. Freezing the last layer allows the network to focus on learning from a more stable representation, enhancing overall learning efficiency.