Experiments with a New Loss Term Added to the Standard Cross entropy

Recently I came across this idea of center loss described in this paper. You define the outputs from the second last layers of the neural network as embeddings. For this loss, you define a per class center which serves as the centroid of embeddings corresponding to that class. As the network gets updated with gradient descent, the per class center term needs to be updated. Ideally the update would involve going through the entire training data, but that is not feasible in practice.