  Deep learning has become an increasingly popular and powerful methodology for
modern pattern recognition systems. However, many deep neural networks have
millions or billions of parameters, making them untenable for real-world
applications due to constraints on memory size or latency requirements. As a
result, efficient network compression techniques are often required for the
widespread adoption of deep learning methods. We present DECORE, a
reinforcement learning-based approach to automate the network compression
process. DECORE assigns an agent to each channel in the network along with a
light policy gradient method to learn which neurons or channels to be kept or
removed. Each agent in the network has just one parameter (keep or drop) to
learn, which leads to a much faster training process compared to existing
approaches. DECORE provides state-of-the-art compression results on various
network architectures and various datasets. For example, on the ResNet-110
architecture, it achieves a 64.8% compression and 61.8% FLOPs reduction as
compared to the baseline model without any accuracy loss on the CIFAR-10
dataset. It can reduce the size of regular architectures like the VGG network
by up to 99% with just a small accuracy drop of 2.28%. For a larger dataset
like ImageNet with just 30 epochs of training, it can compress the ResNet-50
architecture by 44.7% and reduce FLOPs by 42.3%, with just a 0.69% drop on
Top-5 accuracy of the uncompressed model. We also demonstrate that DECORE can
be used to search for compressed network architectures based on various
constraints, such as memory and FLOPs.
