Robust compressed sensing using generative models
Authors: Ajil Jalal, Liu Liu, Alexandros G. Dimakis, Constantine Caramanis
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we study the empirical performance of our algorithm on generative models trained on real image datasets. We show that we can reconstruct images under heavy-tailed samples and arbitrary outliers. For additional experiments and experimental setup details, see Appendix F. |
| Researcher Affiliation | Academia | Ajil Jalal ECE, UT Austin ajiljalal@utexas.edu Liu Liu ECE, UT Austin liuliu@utexas.edu Alexandros G. Dimakis ECE, UT Austin dimakis@austin.utexas.edu Constantine Caramanis ECE, UT Austin constantine@utexas.edu |
| Pseudocode | Yes | Algorithm 1 Robust compressed sensing of generative models 1: Input: Data samples {yj, aj}m j=1. 2: Output: G(bz). 3: Parameters: Number of batches M. 4: Initialize z and z . 5: for t = 0 to T 1, do 6: For each batch j [M], calculate 1 |Bj|(ℓj(z) ℓj(z )) by eq. (1). 7: Pick the batch with the median loss median 1 j M (ℓj(z) ℓj(z )), and evaluate the gradient for z and z using backpropagation on that batch. (i) perform gradient descent for z; (ii) perform gradient ascent for z . 8: end for 9: Output the G(bz) = G(z). |
| Open Source Code | Yes | Link to our code: https://github.com/ajiljalal/csgm-robust-neurips |
| Open Datasets | Yes | We trained a DCGAN [80] with k = 100 and d = 5 layers to produce 64 64 MNIST images. For Celeb A-HQ, we used a PG-GAN [51] with k = 512 to produce images of size 256 256 3 = 196, 608. |
| Dataset Splits | No | The paper uses standard datasets (MNIST, Celeb A-HQ) but does not explicitly provide specific train/validation/test dataset splits (e.g., percentages or sample counts) within the main text for its experiments. |
| Hardware Specification | No | The paper mentions 'computing resources from TACC' but does not provide specific details such as GPU/CPU models, memory, or other detailed computer specifications used for running experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers. It refers to general frameworks like DCGAN [80] and PG-GAN [51] and an optimizer Adam [52], but without explicit version details for libraries or environments. |
| Experiment Setup | Yes | For additional experiments and experimental setup details, see Appendix F. We fix k = 100 for the MNIST dataset and k = 512 for the Celeb A-HQ dataset. We set the outliers of measurement matrix A as random sign matrix, and the outliers of y are fixed to be 1. we fix the number of measurements to m = 1000. |