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.
翻译:我们提出了一个新的深层发展的分析分化压缩遥感网络。拟议建立的解码网络(DEONET)联合学习了一个解码器,该解码器从不完全的、吵闹的测量和冗余的累累式分析操作器中重建矢量,DEONET各层之间共享。此外,我们制定了DEONET的假设类别,并估算了它相关的Rademacher复杂性。然后,我们利用这一估计来为DEONET的概括错误提供有意义的上限。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方面对最新开发的网络进行了比较。实验结果显示,我们拟议的网络超越了基线,对所有数据集来说,其行为都符合我们的理论结论。