What Does Freezing A Layer Mean And How Does It Help In Fine Tuning Neural Networks

Freezing a layer in the context of neural networks is about controlling the way the weights are updated. When a layer is frozen, it means that the weights cannot be modified further. This technique, as obvious as it may sound is to cut down on the computational time for training while losing not much on the accuracy side. Techniques like DropOut and Stochastic depth have already demonstrated how to efficiently train the networks without the need to train every layer. Freezing a layer, too, is a technique to accelerate neural network training by progressively freezing hidden layers. For instance, during transfer learning, the first layer of the network are frozen while leaving the end layers open to modification. This  means that if a machine learning model is tasked with object dete