Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
On The Classification-Distortion-Perception Tradeoff
Authors: Dong Liu, Haochen Zhang, Zhiwei Xiong
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 14,208 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,214 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We rigorously prove the existence of the CDP tradeoff, i.e. the distortion, perceptual difference, and classification error rate cannot be made all minimal simultaneously. We also provide both simulation and experimental results to showcase the CDP tradeoff. |
| Researcher Affiliation | Academia | Dong Liu, Haochen Zhang, Zhiwei Xiong University of Science and Technology of China, Hefei 230027, China EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and models are published at https://github.com/ Alan Zhang1995/CDP-Tradeoff. |
| Open Datasets | Yes | We use the MNIST handwritten digit recognition dataset [11] and the CIFAR-10 image recognition dataset [9]. |
| Dataset Splits | No | The paper mentions training and testing data but does not explicitly provide details about a validation dataset split. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | Yes | In order to showcase the CDP tradeoff, we train a restoration (denoising or SR) network with a combination of three loss functions that correspond to distortion, perceptual difference, and classification error rate. In short, the entire loss function is ℓrestoration = αℓMSE + βℓadv + γℓCE where α, β, γ are weights. ...with different combinations of (α, β, γ). |