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..
A Theory of the Distortion-Perception Tradeoff in Wasserstein Space
Authors: Dror Freirich, Tomer Michaeli, Ron Meir
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we illustrate our results, numerically and visually, in a super-resolution setting in Section 5. The proofs of all our theorems are provided in Appendix B. In Fig. 3 we plot each method on the distortion-perception plane. We consider the EDSR method [14] to constitute a good approximation for the minimum MSE estimator X since it achieves the lowest MSE among the evaluated methods. We therefore estimate the lower bound (9) as ˆD(P) = DEDSR + [(PEDSR P)+]2 , where DEDSR is the MSE of EDSR, and PEDSR is the estimated Gelbrich distance between EDSR reconstructions and ground-truth images. |
| Researcher Affiliation | Academia | Dror Freirich Technion Israel Institute of Technology EMAIL Tomer Michaeli Technion Israel Institute of Technology EMAIL Ron Meir Technion Israel Institute of Technology EMAIL |
| Pseudocode | No | The paper describes algorithms and derivations mathematically but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | All codes are freely available and provided by the authors. |
| Open Datasets | Yes | We compute distortion and perception indices for 13 super resolution algorithms in a 4 magnification task on the BSD100 dataset2 [16]. |
| Dataset Splits | No | The paper mentions evaluating on the BSD100 dataset but does not specify explicit training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the overall approach and the construction of optimal estimators via interpolation, but it does not specify concrete hyperparameters or system-level training settings for their experimental setup (e.g., learning rate, batch size, epochs). |