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..
WBFlow: Few-shot White Balance for sRGB Images via Reversible Neural Flows
Authors: Chunxiao Li, Xuejing Kang, Anlong Ming
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that WBFlow achieves superior camera generalization and accuracy on three public datasets as well as our rendered multiple camera s RGB dataset. |
| Researcher Affiliation | Academia | Chunxiao Li , Xuejing Kang , Anlong Ming School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications EMAIL |
| Pseudocode | No | The paper describes the model architecture and processes but does not provide any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/ChunxiaoLe/WBFlow. |
| Open Datasets | Yes | Following [Afifi and Brown, 2020a], we randomly selected 12000 s RGB images from the first fold of Set1 [Afifi et al., 2019a; Cheng et al., 2014] to train WBFlow. |
| Dataset Splits | No | The paper mentions training and testing on specific datasets (Set1, Set1-Test, Set2, Rendered Cube Dataset) but does not explicitly describe a separate validation set or detailed train/validation/test splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models used for running experiments. |
| Software Dependencies | No | We implemented WBFlow on Pytorch with CUDA support and used the Adam [Kingma and Ba, 2014] with β1 = 0.9 and learning rate 10 4 to optimize it. |
| Experiment Setup | Yes | For the experiments with all training images, we trained WBFlow for 340000 iterations with batch size 4. While for few-shot experiments, we trained CT for 15000 iterations. We used the Adam [Kingma and Ba, 2014] with β1 = 0.9 and learning rate 10 4 to optimize it. We used color jittering, average blur, geometric rotation, and flipping to augment data. During testing, following [Afifi and Brown, 2020a], we resized all input images to a maximum dimension of 656 pixels and set a color mapping procedure to compute the final white-balanced s RGB images. |