  In the last decade convolutional neural networks have become gargantuan.
Pre-trained models, when used as initializers are able to fine-tune ever larger
networks on small datasets. Consequently, not all the convolutional features
that these fine-tuned models detect are requisite for the end-task. Several
works of channel pruning have been proposed to prune away compute and memory
from models that were trained already. Typically, these involve policies that
decide which and how many channels to remove from each layer leading to
channel-wise and/or layer-wise pruning profiles, respectively. In this paper,
we conduct several baseline experiments and establish that profiles from random
channel-wise pruning policies are as good as metric-based ones. We also
establish that there may exist profiles from some layer-wise pruning policies
that are measurably better than common baselines. We then demonstrate that the
top layer-wise pruning profiles found using an exhaustive random search from
one datatset are also among the top profiles for other datasets. This implies
that we could identify out-of-the-box layer-wise pruning profiles using
benchmark datasets and use these directly for new datasets. Furthermore, we
develop a Reinforcement Learning (RL) policy-based search algorithm with a
direct objective of finding transferable layer-wise pruning profiles using many
models for the same architecture. We use a novel reward formulation that drives
this RL search towards an expected compression while maximizing accuracy. Our
results show that our transferred RL-based profiles are as good or better than
best profiles found on the original dataset via exhaustive search. We then
demonstrate that if we found the profiles using a mid-sized dataset such as
Cifar10/100, we are able to transfer them to even a large dataset such as
Imagenet.
