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 [1].
It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems
Authors: Regev Cohen, Yochai Blau, Daniel Freedman, Ehud Rivlin
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here we study the performance of Algorithm 1 using our three proposed potential-driven denoisers: Gra Dn CNN, Dn ICNN, Dn DICNN. We compare ourselves to Pn P-PGD and RED-SD, applied with the popular Dn CNN denoiser [54], for the tasks of Gaussian deblurring and image super resolution. |
| Researcher Affiliation | Industry | Regev Cohen Verily Research, Israel EMAIL Yochai Blau Google Research, Israel EMAIL Daniel Freedman Verily Research, Israel EMAIL Ehud Rivlin Verily Research, Israel EMAIL |
| Pseudocode | Yes | Algorithm 1: Regularization by Potential-Driven Denoising |
| Open Source Code | No | The paper does not include any explicit statement about making its source code available or provide a link to a code repository. |
| Open Datasets | Yes | For training the denoising networks for blind Gaussian denoising we use the public DIV2K dataset [2] that consists of a total of 900 high resolution images, 800 for training and 100 for validation. |
| Dataset Splits | Yes | For training the denoising networks for blind Gaussian denoising we use the public DIV2K dataset [2] that consists of a total of 900 high resolution images, 800 for training and 100 for validation. |
| Hardware Specification | Yes | All experiments are performed in Tensorflow [1] where each model is trained on a single NVIDIA Tesla 32GB V100 GPU. |
| Software Dependencies | No | The paper mentions 'Tensorflow [1]' as the framework used, but does not provide specific version numbers for Tensorflow or any other software dependencies. |
| Experiment Setup | Yes | Given the datasets detailed above, we train each of the networks using an Adam optimizer for 100 epochs with a constant learning rate of 10-3. For the training loss, we use a modified version of mean squared error (MSE) cost function: Pn 1/σ2n MSE(xn − x n, Rθ(xn)). |