29,358 research outputs found
A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks
Deep neural networks (DNNs) have achieved significant success in a variety of
real world applications, i.e., image classification. However, tons of
parameters in the networks restrict the efficiency of neural networks due to
the large model size and the intensive computation. To address this issue,
various approximation techniques have been investigated, which seek for a light
weighted network with little performance degradation in exchange of smaller
model size or faster inference. Both low-rankness and sparsity are appealing
properties for the network approximation. In this paper we propose a unified
framework to compress the convolutional neural networks (CNNs) by combining
these two properties, while taking the nonlinear activation into consideration.
Each layer in the network is approximated by the sum of a structured sparse
component and a low-rank component, which is formulated as an optimization
problem. Then, an extended version of alternating direction method of
multipliers (ADMM) with guaranteed convergence is presented to solve the
relaxed optimization problem. Experiments are carried out on VGG-16, AlexNet
and GoogLeNet with large image classification datasets. The results outperform
previous work in terms of accuracy degradation, compression rate and speedup
ratio. The proposed method is able to remarkably compress the model (with up to
4.9x reduction of parameters) at a cost of little loss or without loss on
accuracy.Comment: 8 pages, 5 figures, 6 table
A Unified View of Piecewise Linear Neural Network Verification
The success of Deep Learning and its potential use in many safety-critical
applications has motivated research on formal verification of Neural Network
(NN) models. Despite the reputation of learned NN models to behave as black
boxes and the theoretical hardness of proving their properties, researchers
have been successful in verifying some classes of models by exploiting their
piecewise linear structure and taking insights from formal methods such as
Satisifiability Modulo Theory. These methods are however still far from scaling
to realistic neural networks. To facilitate progress on this crucial area, we
make two key contributions. First, we present a unified framework that
encompasses previous methods. This analysis results in the identification of
new methods that combine the strengths of multiple existing approaches,
accomplishing a speedup of two orders of magnitude compared to the previous
state of the art. Second, we propose a new data set of benchmarks which
includes a collection of previously released testcases. We use the benchmark to
provide the first experimental comparison of existing algorithms and identify
the factors impacting the hardness of verification problems.Comment: Updated version of "Piecewise Linear Neural Network verification: A
comparative study
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