QCS-SGM+: Improved Quantized Compressed Sensing with Score-Based Generative Models
Authors: Xiangming Meng, Yoshiyuki Kabashima
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted, demonstrating the substantial superiority of QCS-SGM+ over QCS-SGM for general sensing matrices beyond mere row-orthogonality. |
| Researcher Affiliation | Academia | 1The Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, China 2Institute for Physics of Intelligence & Department of Physics, The University of Tokyo, Japan xiangmingmeng@intl.zju.edu.cn, kaba@phys.s.u-tokyo.ac.jp |
| Pseudocode | Yes | Algorithm 1: QCS-SGM+ Input: {βt}T t=1, ϵ, γ, Iter EP, K, y, A, σ2, quantization thresholds {[lq, uq)|q Q} Initialization: x0 1 U (0, 1) 1 for t = 1 to T do 2 αt ϵβ2 t /β2 T 3 for k = 1 to K do 4 Draw zk t N(0, I) Initialization: h F , τ F , h G, τ G 5 for it = 1 to Iter EP do 7 τ G = 1 χa τ F 9 τ F = 1 χb τ G 10 Compute xt log p(y | xt) as (23) 11 xk t = xk 1 t + αt h sθ(xk 1 t , βt) + γ xt log p(y | xt) i + 2αtzk t 12 x0 t+1 x K t Output: ˆx = x K T |
| Open Source Code | Yes | The source code is available at https://github.com/mengxiangming/QCS-SGM-plus. |
| Open Datasets | Yes | Datasets: We consider three popular datasets: MNIST (Le Cun and Cortes 2010) , CIFAR-10 (Krizhevsky and Hinton 2009), and Celeb A (Liu et al. 2015). |
| Dataset Splits | No | The paper uses standard datasets but does not explicitly state the train/validation/test splits or their methodology for splitting. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using NCSNv2 but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | The paper specifies various experimental settings and hyperparameters, including the number of measurements (M), noise standard deviation (σ), condition number (κ), and algorithm parameters such as {βt}T t=1, ϵ, γ, Iter EP, K, and quantization thresholds {[lq, uq)|q Q} as shown in Algorithm 1 and figure captions (e.g., Figure 2: 'M = 400, σ = 0.05, κ = 103' for MNIST). |