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..
Tensor FISTA-Net for Real-Time Snapshot Compressive Imaging
Authors: Xiaochen Han, Bo Wu, Zheng Shou, Xiao-Yang Liu, Yimeng Zhang, Linghe Kong10933-10940
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic datasets show that the proposed Tensor FISTA-Net achieves average PSNR improvement of 1.63 3.89d B over the state-of-the-art algorithms. |
| Researcher Affiliation | Academia | Xiaochen Han,1 Bo Wu,2 Zheng Shou,2 Xiao-Yang Liu,2 Yimeng Zhang,2 Linghe Kong1 1Shanghai Jiao Tong University, China 2Columbia University, USA |
| Pseudocode | No | The paper describes the iterative steps and modules of Tensor FISTA-Net through mathematical equations and descriptions, but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | Yes | The code is available at https://github.com/Guillermo Han97/SCI-Tensor-FISTA-Net. |
| Open Datasets | Yes | We evaluate the proposed Tensor FISTA-Net on three different synthetic datasets: Kobe (Yang et al. 2014), Park (Ma et al. 2019) and Vehicle (Ma et al. 2019). |
| Dataset Splits | No | Each testing dataset contains 32 frames of size 256 256 and B = 8, i.e., 4 measurements. We use the same training videos NBA, Central Park Aerial and Vehicle Crashing Tests in (Ma et al. 2019). |
| Hardware Specification | Yes | We do all the experiments on a server with an NVIDIA Telsa V100-PCIE GPU (16GB device memory). |
| Software Dependencies | No | We use Tensor Flow to implement our algorithm and do all the experiments on a server with an NVIDIA Telsa V100-PCIE GPU (16GB device memory). ... Adam optimizer (Kingma and Ba 2014) is used to minimize the training loss. |
| Experiment Setup | Yes | We set the number of phases as 10, learning rate as 0.0001 and running epoch as 500. Adam optimizer (Kingma and Ba 2014) is used to minimize the training loss. |