23,139 research outputs found
Deep unrolling networks with recurrent momentum acceleration for nonlinear inverse problems
Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, deep unrolling networks (DuNets) have become a popular tool to solve inverse imaging problems. Although DuNets have been successfully applied to many linear inverse problems, their performance tends to be impaired by nonlinear problems. Inspired by momentum acceleration techniques that are often used in optimization algorithms, we propose a recurrent momentum acceleration (RMA) framework that uses a long short-term memory recurrent neural network (LSTM-RNN) to simulate the momentum acceleration process. The RMA module leverages the ability of the LSTM-RNN to learn and retain knowledge from the previous gradients. We apply RMA to two popular DuNets—the learned proximal gradient descent (LPGD) and the learned primal-dual (LPD) methods, resulting in LPGD-RMA and LPD-RMA, respectively. We provide experimental results on two nonlinear inverse problems: a nonlinear deconvolution problem, and an electrical impedance tomography problem with limited boundary measurements. In the first experiment we have observed that the improvement due to RMA largely increases with respect to the nonlinearity of the problem. The results of the second example further demonstrate that the RMA schemes can significantly improve the performance of DuNets in strongly ill-posed problems
Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems
Combining the strengths of model-based iterative algorithms and data-driven
deep learning solutions, deep unrolling networks (DuNets) have become a popular
tool to solve inverse imaging problems. While DuNets have been successfully
applied to many linear inverse problems, nonlinear problems tend to impair the
performance of the method. Inspired by momentum acceleration techniques that
are often used in optimization algorithms, we propose a recurrent momentum
acceleration (RMA) framework that uses a long short-term memory recurrent
neural network (LSTM-RNN) to simulate the momentum acceleration process. The
RMA module leverages the ability of the LSTM-RNN to learn and retain knowledge
from the previous gradients. We apply RMA to two popular DuNets -- the learned
proximal gradient descent (LPGD) and the learned primal-dual (LPD) methods,
resulting in LPGD-RMA and LPD-RMA respectively. We provide experimental results
on two nonlinear inverse problems: a nonlinear deconvolution problem, and an
electrical impedance tomography problem with limited boundary measurements. In
the first experiment we have observed that the improvement due to RMA largely
increases with respect to the nonlinearity of the problem. The results of the
second example further demonstrate that the RMA schemes can significantly
improve the performance of DuNets in strongly ill-posed problems
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
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