Scalable Deep Compressive Sensing
Authors: Zhonghao Zhang, Yipeng Liu, Xingyu Cao, Fei Wen, Ce Zhu
TMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that models with SDCS can achieve SSR without changing their structure while maintaining good performance, and SDCS outperforms other SSR methods. [...] Table 1 and Table 2 show the average PSNR and SSIM of 12 models tested on Set11 and the testing set of BSDS500 at different CS ratios respectively. |
| Researcher Affiliation | Collaboration | Zhonghao Zhang EMAIL School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China. [...] Xingyu Cao EMAIL Alibaba DAMO Academy. [...] Fei Wen EMAIL Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China. |
| Pseudocode | Yes | Algorithm 1 Scalable training of one epoch. Input: training set T, batch size B, loss function L, max CS ratio RM, sampling matrix A, initialization matrix B, reconstruction model Ftra( ; Θ) or Funf( ; A, Θ). Output: trained parameters. 1: T 2: repeat 3: Select S = {X1, X2, , XB} T \ T . 4: T T S. 5: Generate {R1, R2, , RB} randomly, where Ri [1, RM]. 6: Generate {M1, M2, , MB}, where Mi(1 : Ri N , :) = 1 and Mi( Ri N + 1 : RMN , :) = 0. 7: Generate AS = {AS1, AS2, , ASB}, AR = {AR1, AR2, , ARB} and BR = {BR1, BR2, , BRB}, where ASi = Mi A, ARi = Mi A and BRi = MT i B. 8: for i = 1 : B do 9: yi = ASi vec(Xi) 10: X0 i = vec 1(BRiyi) 11: ˆXi = Ftra(X0 i ; Θ) or ˆXi = Funf(X0 i , yi; ARi, Θ) 12: Compute loss L using { ˆX1, ˆX2, , ˆXB} and S. 13: Update A, B and Θ. 14: until T \ T = 15: return A, B, Θ. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing code, a link to a code repository, or mention of code in supplementary materials. |
| Open Datasets | Yes | All of our experiments are performed on two datasets: BSDS500 (Arbelaez et al., 2010) and Set11 (Lohit et al., 2018a). BSDS500 contains 500 colorful visual images and is composed of a training set (200 images), a validation set (100 images) and a test set (200 images). |
| Dataset Splits | Yes | BSDS500 contains 500 colorful visual images and is composed of a training set (200 images), a validation set (100 images) and a test set (200 images). [...] We generate two training sets for models with and without trainable deblocking operations. (a) Training set 1 contains 89600 sub-images sized of 99 99 which are randomly extracted from the luminance components of images in the training set of BSDS500 (Shi et al., 2019a). (b) Training set 2 contains 195200 sub-images sized of 33 33 which are randomly extracted from the luminance components of images in the training set of BSDS500 (Zhang & Ghanem, 2018). |
| Hardware Specification | Yes | All experiments are performed on a computer with an AMD Ryzen7 2700X CPU and an RTX2080Ti GPU. |
| Software Dependencies | No | The paper mentions algorithms like Adam optimizer but does not specify any software names with version numbers for implementation, programming languages, or libraries. |
| Experiment Setup | Yes | In this paper, the model combined with SDCS is named as model-SDCS. [...] RM is 50% and RVG is {1%, 4%, 10%, 25%, 30%, 40%, 50%}. [...] All sampling matrices are initialized randomly in Gaussian distribution. [...] In detail, additive Gaussian white noises (Lepskii, 1991) are added to y of all datasets to train and test models in the subsection. And the signal-to-noise ratios (SNRs) are 40d B, 30d B, 25d B and 15d B. |