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..
From Patches to Images: A Nonparametric Generative Model
Authors: Geng Ji, Michael C. Hughes, Erik B. Sudderth
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our denoising performance on standard benchmarks is superior to EPLL and comparable to the state-of-the-art, and we provide novel statistical justifications for common image processing heuristics. We also show accurate image inpainting results. |
| Researcher Affiliation | Academia | 1Brown University, Providence, RI, USA. 2Harvard University, Cambridge, MA, USA. 3University of California, Irvine, CA, USA. |
| Pseudocode | No | The paper describes methods and processes but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | Yes | Our open-source Python code is available online at github.com/bnpy/hdp-grid-image-restoration. |
| Open Datasets | Yes | Following EPLL, we train our HDP-Grid model using 400 clean training and validation images from the Berkeley segmentation dataset (BSDS, Martin et al. (2001)). |
| Dataset Splits | No | Following EPLL, we train our HDP-Grid model using 400 clean training and validation images from the Berkeley segmentation dataset (BSDS, Martin et al. (2001)). |
| Hardware Specification | No | To denoise a 512 512 pixel image on a modern laptop, our Python code for e DP inference with K = 449 clusters takes about 12 min. |
| Software Dependencies | No | Our open-source Python code is available online at github.com/bnpy/hdp-grid-image-restoration. |
| Experiment Setup | Yes | We fix δ = 0.5/255 to account for the quantization of image intensities to 8-bit integers. We initialize inference by creating K = 100 image-specific clusters with the k-means++ algorithm (Arthur & Vassilvitskii, 2007)... and refine with 50 iterations of coordinate descent updates... We set our annealing schedule for κ to match that used by the public EPLL code. |