mlsanityData to 14 Oct 2026

Generator Surgery for Compressed Sensing

arXiv 2102.11163 ↗First posted 22 Feb 2021cs.CV eess.IVcited by 5

Not on the map. Why?

Abstract (arXiv)

Image recovery from compressive measurements requires a signal prior for the images being reconstructed. Recent work has explored the use of deep generative models with low latent dimension as signal priors for such problems. However, their recovery performance is limited by high representation error. We introduce a method for achieving low representation error using generators as signal priors. Using a pre-trained generator, we remove one or more initial blocks at test time and optimize over the new, higher-dimensional latent space to recover a target image. Experiments demonstrate significantly improved reconstruction quality for a variety of network architectures. This approach also works well for out-of-training-distribution images and is competitive with other state-of-the-art methods. Our experiments show that test-time architectural modifications can greatly improve the recovery quality of generator signal priors for compressed sensing.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 15 of its 19 references that are in the map. First posted on arXiv 22 Feb 2021.

Builds on

15 of 19 references.

References · 15

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
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  3. 2014Deep Learning Face Attributes in the Wildcited by 3,693
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  8. 2019Image Processing Using Multi-Code GAN Priorcited by 185
  9. 2016Inverting The Generator Of A Generative Adversarial Networkcited by 140
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  11. 2018Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networkscited by 120
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3 more
  1. 2019Image-Adaptive GAN based Reconstructioncited by 36
  2. 2018Latent Convolutional Modelscited by 6
  3. 2020Reducing the Representation Error of GAN Image Priors Using the Deep Decodercited by 1

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Cited by year

Citing papers per year: 2022: 4; 2023: 0; 2024: 1.

5 papers.

  1. 2022Multiresolution Textual Inversioncited by 34
  2. 2024A Closer Look at GAN Priors: Exploiting Intermediate Features for Enhanced Model Inversion Attackscited by 18
  3. 2022Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problemscited by 11
  4. 2022Regularized Training of Intermediate Layers for Generative Models for Inverse Problemscited by 1
  5. 2022Optimizing Intermediate Representations of Generative Models for Phase Retrievalcited by 1