Adaptive Denoising via GainTuning
Authors: Sreyas Mohan, Joshua L Vincent, Ramon Manzorro, Peter Crozier, Carlos Fernandez-Granda, Eero Simoncelli
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | All claims are supported by extensive empirical experiments |
| Researcher Affiliation | Academia | Sreyas Mohan1, Joshua L. Vincent2, Ramon Manzorro2, Peter A. Crozier 2, Carlos Fernandez-Granda1,3, Eero P. Simoncelli1,3,4 1Center For Data Science, NYU, 2School for Engineering of Matter, Transport and Energy, ASU 3Courant Institute of Mathematical Sciences, NYU 4Center for Neural Science, NYU and Flatiron Institute, Simons Foundation |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code will be made available at https://github.com/sreyas-mohan/gaintuning |
| Open Datasets | Yes | Our experiments make use of four datasets: The BSD400 natural image database [34] with test sets Set12 and Set68 [66], the Urban100 images of urban environments [22], the IUPR dataset of scanned documents [4], and a set of synthetic piecewise constant images [31] (see Section B). |
| Dataset Splits | No | The paper mentions test sets and training datasets but does not explicitly provide specific validation dataset split information (e.g., percentages, sample counts, or explicit mention of a validation set). |
| Hardware Specification | No | The paper mentions the use of 'high performance computing resources' from ASU Research Computing and NYU HPC, but does not provide specific hardware details such as GPU/CPU models, memory, or other detailed specifications for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | No | The paper states that training details including hyperparameters are specified in Section A, B, C, but these sections are not provided within the main text of the paper. Therefore, concrete hyperparameter values or detailed training configurations are not explicitly present. |