In this work, we consider the problem of one-bit deep compressive sensing from both a system design and a signal recovery perspective. In particular, we develop hybrid model-based deep learning architectures based on the deep unfolding methodology. We further interpret the overall data-acquisition and signal recovery modules as an auto-encoder structure allowing for learning task-specific sensing matrix, quantization thresholds, as well as the latent-parameters of iterative first-order optimization algorithms specifically designed for the problem of one-bit sparse signal recovery. The proposed model-based deep architectures have the ability to adaptively learn the proper quantization thresholds, paving the way for amplitude recovery in one-bit compressive sensing. We further show that the proposed methodology implicitly learns task-specific sensing matrices with very low coherence, which is highly desirable in a compressive sensing setting. Due to the model-based nature of the proposed deep architecture, it enjoys from the interpretability and versatility of model-based techniques as well as benefiting from the expressive power of data-driven methods. Specifically, owing to its model-based nature, it has far fewer parameters and requires far less samples for training as compared to black-box machine learning models. Our results demonstrate a significant improvement compared to state-of-the-art algorithms.

中文翻译：

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用于一位压缩感知的基于模型的深度学习

在这项工作中，我们从系统设计和信号恢复的角度考虑了一位深度压缩感知的问题。特别是，我们基于深度展开方法开发了基于混合模型的深度学习架构。我们进一步将整体数据采集和信号恢复模块解释为一个自动编码器结构，允许学习特定任务的传感矩阵、量化阈值以及专为解决问题而设计的迭代一阶优化算法的潜在参数。一位稀疏信号恢复。所提出的基于模型的深度架构能够自适应地学习适当的量化阈值，为一位压缩感知中的幅度恢复铺平了道路。我们进一步表明，所提出的方法隐式地学习具有非常低相干性的特定于任务的传感矩阵，这在压缩传感设置中是非常理想的。由于所提出的深层架构的基于模型的性质，它享有基于模型技术的可解释性和多功能性，以及受益于数据驱动方法的表达能力。具体来说，由于其基于模型的性质，与黑盒机器学习模型相比，它具有更少的参数和更少的训练样本。我们的结果表明与最先进的算法相比有了显着的改进。它受益于基于模型的技术的可解释性和多功能性，以及受益于数据驱动方法的表达能力。具体来说，由于其基于模型的性质，与黑盒机器学习模型相比，它具有更少的参数并且需要更少的训练样本。我们的结果表明与最先进的算法相比有了显着的改进。它受益于基于模型的技术的可解释性和多功能性，以及受益于数据驱动方法的表达能力。具体来说，由于其基于模型的性质，与黑盒机器学习模型相比，它具有更少的参数并且需要更少的训练样本。我们的结果表明与最先进的算法相比有了显着的改进。