论文标题
DECONET：用于基于分析的压缩传感的发展网络，具有概括性误差界限
DECONET: an Unfolding Network for Analysis-based Compressed Sensing with Generalization Error Bounds
论文作者
论文摘要
我们提出了一个新的深层展开网络，用于基于分析的压缩传感。提出的网络创造的解码网络（Deconet）共同学习了一个解码器，该解码器从其不完整的，嘈杂的测量值和冗余的稀疏分析操作员中重建向量，该分析运算符在Deconet层中共享。此外，我们制定了Deconet的假设类别，并估计其相关的Rademacher复杂性。然后，我们使用此估计来提供有意义的上限，以实现Deconet的概括误差。最后，评估了我们的理论结果的有效性，并在合成和现实世界数据集上进行了与最新展开网络的比较。实验结果表明，对于所有数据集，我们提出的网络始终优于基准，其行为符合我们的理论发现。
We present a new deep unfolding network for analysis-sparsity-based Compressed Sensing. The proposed network coined Decoding Network (DECONET) jointly learns a decoder that reconstructs vectors from their incomplete, noisy measurements and a redundant sparsifying analysis operator, which is shared across the layers of DECONET. Moreover, we formulate the hypothesis class of DECONET and estimate its associated Rademacher complexity. Then, we use this estimate to deliver meaningful upper bounds for the generalization error of DECONET. Finally, the validity of our theoretical results is assessed and comparisons to state-of-the-art unfolding networks are made, on both synthetic and real-world datasets. Experimental results indicate that our proposed network outperforms the baselines, consistently for all datasets, and its behaviour complies with our theoretical findings.
