Distilling-the-knowledge-in-neural-network

Teaches a student network from the knowledge obtained via training of a larger teacher network

This is an implementation of the paper "Distilling the Knowledge in a Neural Network" arXiv preprint arXiv:1503.02531v1 (2015).

Running distill.py first trains a CNN network till 20k steps and then uses the prediction of this network as soft targets for a student network comprising of a single hidden fc layer . The student network trained using this way achieves a test accuracy of 96.55%.

The student network when trained directly without any knowledge from the teacher network achieves an accuracy of only 94.08% . This can be seen by running student.py.

Thus using the knowledge from another network we see an improvement in test accuracy of around 2.5% .