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..
Domain Adversarial Learning for Color Constancy
Authors: Zhifeng Zhang, Xuejing Kang, Anlong Ming
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that the proposed DALCC can more effectively take advantage of multi-domain data and thus achieve state-of-the-art performance on commonly used benchmark datasets. |
| Researcher Affiliation | Academia | School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications EMAIL |
| Pseudocode | No | The paper describes the proposed modules textually but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link for open-sourcing the code for the described methodology. |
| Open Datasets | Yes | We verify the effectiveness of our proposed DALCC on two public datasets, the NUS-8 dataset[Cheng et al., 2014] and the Cube+ dataset[Bani c et al., 2017]. |
| Dataset Splits | Yes | Following[Tang et al., 2022; Xiao et al., 2020], we adopt the three-fold crossvalidation in all experiments. |
| Hardware Specification | No | The paper mentions implementing the network on Pytorch with CUDA support, implying GPU usage, but does not specify any particular GPU models, CPU, or other hardware details used for experiments. |
| Software Dependencies | No | The paper mentions 'Pytorch with CUDA support' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We train our model about 3000 epochs by setting the learning rate to 1 10 4. The batch size is 16. We use Adam to optimize the network. |