Quantized Compressed Sensing with Score-Based Generative Models
Authors: Xiangming Meng, Yoshiyuki Kabashima
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a variety of baseline datasets demonstrate that the proposed QCS-SGM significantly outperforms existing state-of-the-art algorithms by a large margin for both in-distribution and out-of-distribution samples. Moreover, as a posterior sampling method, QCS-SGM can be easily used to obtain confidence intervals or uncertainty estimates of the reconstructed results. The code is available at https://github.com/mengxiangming/QCS-SGM. |
| Researcher Affiliation | Academia | Xiangming Meng and Yoshiyuki Kabashima Institute for Physics of Intelligence and Department of Physics The University of Tokyo 7-3-1, Hongo, Tokyo 113-0033, Japan {meng,kaba}@g.ecc.u-tokyo.ac.jp |
| Pseudocode | Yes | Algorithm 1: Quantized Compressed Sensing with SGM (QCS-SGM) |
| Open Source Code | Yes | The code is available at https://github.com/mengxiangming/QCS-SGM. |
| Open Datasets | Yes | Datasets: Three popular datasets are considered: MNIST (Le Cun & Cortes, 2010) , Cifar-10 (Krizhevsky & Hinton, 2009), and Celeb A (Liu et al., 2015), and the high-resolution Flickr Faces High Quality (FFHQ) (Karras et al., 2018). |
| Dataset Splits | Yes | Results are averaged over a validation set of size 100. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., specific GPU models, CPU types, or memory amounts). |
| Software Dependencies | No | The paper mentions 'NCSNv2 (Song & Ermon, 2020)' and refers to external links for pre-trained models, but it does not specify version numbers for any software libraries, programming languages, or other dependencies used to implement or run the experiments. |
| Experiment Setup | Yes | When performing posterior sampling using the QCS-SGM in 1, for simplicity, we set a constant value ϵ = 0.0002 for all quantized measurements (e.g., 1-bit, 2-bit, 3-bit) for MNIST, Cifar10 and Celeb A. For the high-resolution FFHQ 256 256, we set ϵ = 0.00005 for 1-bit and ϵ = 0.00002 for 2-bit and 3-bit case, respectively. For all linear measurements for MNIST, Cifar10, and Celeb A, we set ϵ = 0.00002. It is believed that some improvement can be achieved with further fine-tuning of ϵ for different scenarios. For MNIST and Cifar-10, we set β1 = 50, βT = 0.01, T = 232; for Celeb A, we set β1 = 90, βT = 0.01, T = 500; for FFHQ, we set β1 = 348, βT = 0.01, T = 2311 which are the same as Song & Ermon (2020). The number of steps K in QCS-SGM for each noise scale is set to be K = 5 in all experiments. For more details, please refer to the submitted code. In training NCSNv2 for MNIST, we used a similar training setup as Song & Ermon (2020) for Cifar10 as follows. Training: batch-size: 128 n-epochs: 500000 n-iters: 300001 snapshot-freq: 50000 snapshot-sampling: true anneal-power: 2 log-all-sigmas: false. |