A Theory of the Distortion-Perception Tradeoff in Wasserstein Space
Authors: Dror Freirich, Tomer Michaeli, Ron Meir
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 drorfrc@gmail.com Tomer Michaeli Technion Israel Institute of Technology tomer.m@ee.technion.ac.il Ron Meir Technion Israel Institute of Technology rmeir@ee.technion.ac.il |
| 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). |