Title: Data-Driven Sparse Structure Selection for Deep Neural Networks,
Abstract: Deep convolutional neural networks have liberated its extraordinary power on
various tasks. However, it is still very challenging to deploy state-of-the-art
models into real-world applications due to their high computational complexity.
How can we design a compact and effective network without massive experiments
and expert knowledge? In this paper, we propose a simple and effective
framework to learn and prune deep models in an end-to-end manner. In our
framework, a new type of parameter -- scaling factor is first introduced to
scale the outputs of specific structures, such as neurons, groups or residual
blocks. Then we add sparsity regularizations on these factors, and solve this
optimization problem by a modified stochastic Accelerated Proximal Gradient
(APG) method. By forcing some of the factors to zero, we can safely remove the
corresponding structures, thus prune the unimportant parts of a CNN. Comparing
with other structure selection methods that may need thousands of trials or
iterative fine-tuning, our method is trained fully end-to-end in one training
pass without bells and whistles. We evaluate our method, Sparse Structure
Selection with several state-of-the-art CNNs, and demonstrate very promising
results with adaptive depth and width selection.