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..
Adaptive Domain Learning for Cross-domain Image Denoising
Authors: Zian Qian, Chenyang Qi, Ka Law, Hao Fu, Chenyang Lei, Qifeng Chen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on public datasets with various smartphone and DSLR cameras, which show our proposed model outperforms prior work on cross-domain image denoising, given a small amount of image data from the target domain sensor. |
| Researcher Affiliation | Collaboration | Zian Qian1 Chenyang Qi1 Ka Lung Law2 Hao Fu2 Chenyang Lei3 Qifeng Chen1 1HKUST 2Sense Time 3CAIR, HKISI-CAS |
| Pseudocode | Yes | Algorithm 1 Adaptive domain learning (ADL) |
| Open Source Code | No | Justification: We will release the code in the future. |
| Open Datasets | Yes | We conduct extensive experiments on public datasets with various smartphone and DSLR cameras... SIDD [2] is a popular RAW denoising dataset... ELD Dataset [42] contains RAW data... SID dataset [5] captured RAW data... |
| Dataset Splits | Yes | At the beginning of the training, k is set to 20% of the size of T adp and increases during the training process. At the end of the training, 50% of T adp will be used. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or cloud instance types) are provided for the experimental setup. |
| Software Dependencies | No | The paper mentions 'Adam W as the optimizer' but does not provide version numbers for any software dependencies, libraries, or programming languages. |
| Experiment Setup | Yes | We train our ADL in 300k iterations, using Adam W as the optimizer with a learning rate of 3 10 3. |