  Pruning generates sparse networks by setting parameters to zero. In this work
we improve one-shot pruning methods, applied before training, without adding
any additional storage costs while preserving the sparse gradient computations.
The main difference to pruning is that we do not sparsify the network's weights
but learn just a few key parameters and keep the other ones fixed at their
random initialized value. This mechanism is called freezing the parameters.
Those frozen weights can be stored efficiently with a single 32bit random seed
number. The parameters to be frozen are determined one-shot by a single for-
and backward pass applied before training starts. We call the introduced method
FreezeNet. In our experiments we show that FreezeNets achieve good results,
especially for extreme freezing rates. Freezing weights preserves the gradient
flow throughout the network and consequently, FreezeNets train better and have
an increased capacity compared to their pruned counterparts. On the
classification tasks MNIST and CIFAR-10/100 we outperform SNIP, in this setting
the best reported one-shot pruning method, applied before training. On MNIST,
FreezeNet achieves 99.2% performance of the baseline LeNet-5-Caffe
architecture, while compressing the number of trained and stored parameters by
a factor of x 157.
