Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Robust compressed sensing using generative models
Authors: Ajil Jalal, Liu Liu, Alexandros G. Dimakis, Constantine Caramanis
NeurIPS 2020 | Venue PDF | 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 EMAIL Liu Liu ECE, UT Austin EMAIL Alexandros G. Dimakis ECE, UT Austin EMAIL Constantine Caramanis ECE, UT Austin EMAIL |
| 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. |