  Neural networks have been notorious for being computationally expensive. This
is mainly because neural networks are often over-parametrized and most likely
have redundant nodes or layers as they are getting deeper and wider. Their
demand for hardware resources prohibits their extensive use in embedded devices
and puts restrictions on tasks like real-time image classification or object
detection. In this work, we propose a network-agnostic model compression method
infused with a novel dynamical clustering approach to reduce the computational
cost and memory footprint of deep neural networks. We evaluated our new
compression method on five different state-of-the-art image classification and
object detection networks. In classification networks, we pruned about 95% of
network parameters. In advanced detection networks such as YOLOv3, our proposed
compression method managed to reduce the model parameters up to 59.70% which
yielded 110X less memory without sacrificing much in accuracy.
