Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems

Core Concepts

Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, the authors 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 and improve the performance of deep unrolling networks (DuNets) in solving nonlinear inverse problems.

Abstract

The content discusses the application of deep unrolling networks (DuNets) to solve nonlinear inverse problems. DuNets combine traditional model-based optimization algorithms with learning-based deep neural networks, providing an interpretable and efficient deep learning framework.
The authors identify that the performance of DuNets tends to be impaired by nonlinear problems, as the gradient of the forward operator varies significantly during the iterations. To address this, they propose a recurrent momentum acceleration (RMA) framework that uses an LSTM-RNN to simulate the momentum acceleration process and leverage the ability of the LSTM-RNN to learn and retain knowledge from the previous gradients.
The RMA module is applied to two popular DuNets - the learned proximal gradient descent (LPGD) and the learned primal-dual (LPD) methods, resulting in LPGD-RMA and LPD-RMA. Experimental results on two nonlinear inverse problems - a nonlinear deconvolution problem and an electrical impedance tomography (EIT) problem with limited boundary measurements - demonstrate that the RMA schemes can significantly improve the performance of DuNets, especially for highly nonlinear problems.
The authors also investigate the sensitivity of the RMA module to the network structure and the data size, and show that the RMA-based methods are more robust and data-efficient compared to the standard DuNet methods.

Stats

The nonlinear deconvolution problem is defined as y(x) = a · x'W2x + w'1x + b, where w1 is the first-order Volterra kernel, W2 is the second-order Volterra kernel, and a controls the degree of nonlinearity.
The EIT problem aims to reconstruct the conductivity distribution σ from boundary voltage measurements y = F(σ) + η, where F is the nonlinear forward operator and η is measurement noise.

Quotes

"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."
"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."

Key Insights Distilled From

Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems

by Qingping Zho... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2307.16120.pdf

Deeper Inquiries

How can the proposed RMA framework be extended to other types of deep unrolling networks beyond LPGD and LPD

The proposed RMA framework can be extended to other types of deep unrolling networks by integrating the recurrent momentum acceleration module into their architectures. For example, in the context of inverse problems, other deep unrolling networks like learned iterative shrinkage-thresholding algorithms (LISTA) or learned ADMM (LADMM) could benefit from the RMA approach. By incorporating the LSTM-RNN to capture and retain information from previous iterations, these networks can improve their performance in handling nonlinear inverse problems. The key is to adapt the RMA module to suit the specific characteristics and requirements of each type of deep unrolling network, ensuring seamless integration and enhanced results.

What are the potential limitations of the RMA approach, and how can it be further improved to handle more challenging nonlinear inverse problems

One potential limitation of the RMA approach is the complexity introduced by the LSTM-RNN module, which may increase the computational overhead and training time. To address this, optimization techniques such as model pruning or quantization can be applied to reduce the computational burden without compromising performance. Additionally, the RMA approach may face challenges in handling extremely nonlinear inverse problems where the gradient variations are significant and complex. To overcome this, further research could focus on developing adaptive learning rate strategies within the RMA module to dynamically adjust the momentum coefficients based on the problem's nonlinearity. Incorporating additional regularization techniques or exploring hybrid models that combine RMA with other optimization methods could also enhance the RMA approach's robustness and effectiveness in handling challenging nonlinear inverse problems.

What other applications beyond inverse problems could benefit from the integration of recurrent neural networks and momentum acceleration techniques in deep learning architectures

Beyond inverse problems, the integration of recurrent neural networks and momentum acceleration techniques in deep learning architectures can benefit various applications in different domains. One such application is natural language processing (NLP), where RNNs are commonly used for sequence modeling tasks. By incorporating momentum acceleration, NLP models can learn to capture long-range dependencies more effectively, leading to improved performance in tasks like machine translation, sentiment analysis, and text generation. Additionally, in computer vision, integrating RNNs with momentum acceleration can enhance video analysis tasks such as action recognition, video captioning, and anomaly detection by leveraging temporal information efficiently. Furthermore, in reinforcement learning, combining RNNs with momentum acceleration can enable agents to learn and adapt to complex environments more efficiently, leading to better decision-making and policy optimization in dynamic scenarios. Overall, the integration of these techniques can enhance the capabilities of deep learning models across a wide range of applications beyond inverse problems.