2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-6.5
Paper Title SPARSITY DRIVEN LATENT SPACE SAMPLING FOR GENERATIVE PRIOR BASED COMPRESSIVE SENSING
Authors Vinayak Killedar, Praveen Kumar Pokala, Chandra Sekhar Seelamantula, Indian Institute of Science, India
SessionMLSP-6: Compressed Sensing and Learning
LocationGather.Town
Session Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-DEEP] Deep learning techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract We address the problem of recovering signals from compressed measurements based on generative priors. Recently, generative-model based compressive sensing (GMCS) methods have shown superior performance over the traditional compressive sensing (CS) techniques in recovering the signals from fewer measurements. However, it is possible to further improve the performance of GMCS by introducing controlled sparsity in the latent-space. We propose a proximal meta-learning (PML) algorithm to enforce sparsity in the latent-space while training the generator. Enforcing sparsity naturally leads to a union of submanifolds model in the solution space. The overall framework is named as sparsity driven latent sampling (SDLS). In addition, we derive the sample complexity bounds for the proposed model. Further more, we demonstrate the efficacy of the proposed framework over the state-of-the-art techniques with application to CS on standard datasets such as MNIST and CIFAR-10. In particular, we evaluate the performance of the proposed method as a function of the number of measurements and sparsity factor in the latent space using standard objective measures. Our findings show that the proposed approach improves the accuracy and aids in faster recovery of the signal in GMCS.