  We propose multirate training of neural networks: partitioning neural network
parameters into "fast" and "slow" parts which are trained on different time
scales, where slow parts are updated less frequently. By choosing appropriate
partitionings we can obtain substantial computational speed-up for transfer
learning tasks. We show for applications in vision and NLP that we can
fine-tune deep neural networks in almost half the time, without reducing the
generalization performance of the resulting models. We analyze the convergence
properties of our multirate scheme and draw a comparison with vanilla SGD. We
also discuss splitting choices for the neural network parameters which could
enhance generalization performance when neural networks are trained from
scratch. A multirate approach can be used to learn different features present
in the data and as a form of regularization. Our paper unlocks the potential of
using multirate techniques for neural network training and provides several
starting points for future work in this area.
