Title: Structural Compression of Convolutional Neural Networks Based on Greedy Filter Pruning,
Abstract: Convolutional neural networks (CNNs) have state-of-the-art performance on
many problems in machine vision. However, networks with superior performance
often have millions of weights so that it is difficult or impossible to use
CNNs on computationally limited devices or to humanly interpret them. A myriad
of CNN compression approaches have been proposed and they involve pruning and
compressing the weights and filters. In this article, we introduce a greedy
structural compression scheme that prunes filters in a trained CNN. We define a
filter importance index equal to the classification accuracy reduction (CAR) of
the network after pruning that filter (similarly defined as RAR for
regression). We then iteratively prune filters based on the CAR index. This
algorithm achieves substantially higher classification accuracy in AlexNet
compared to other structural compression schemes that prune filters. Pruning
half of the filters in the first or second layer of AlexNet, our CAR algorithm
achieves 26% and 20% higher classification accuracies respectively, compared to
the best benchmark filter pruning scheme. Our CAR algorithm, combined with
further weight pruning and compressing, reduces the size of first or second
convolutional layer in AlexNet by a factor of 42, while achieving close to
original classification accuracy through retraining (or fine-tuning) network.
Finally, we demonstrate the interpretability of CAR-compressed CNNs by showing
that our algorithm prunes filters with visually redundant functionalities. In
fact, out of top 20 CAR-pruned filters in AlexNet, 17 of them in the first
layer and 14 of them in the second layer are color-selective filters as opposed
to shape-selective filters. To our knowledge, this is the first reported result
on the connection between compression and interpretability of CNNs.