Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks

Understand

The success of deep neural networks in many real-world applications is leading to new challenges in building more efficient architectures.

• One effective way of making networks more efficient is neural network compression.
• We provide an overview of existing neural network compression methods that can be used to make neural networks more efficient by changing the architecture of the network.
• First, we introduce a new way to categorize all published compression methods, based on the amount of data and compute needed to make the methods work in practice.