  Recent advances in deep learning have made available large, powerful
convolutional neural networks (CNN) with state-of-the-art performance in
several real-world applications. Unfortunately, these large-sized models have
millions of parameters, thus they are not deployable on resource-limited
platforms (e.g. where RAM is limited). Compression of CNNs thereby becomes a
critical problem to achieve memory-efficient and possibly computationally
faster model representations. In this paper, we investigate the impact of lossy
compression of CNNs by weight pruning and quantization, and lossless weight
matrix representations based on source coding. We tested several combinations
of these techniques on four benchmark datasets for classification and
regression problems, achieving compression rates up to $165$ times, while
preserving or improving the model performance.
