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..
Seeing Dark Videos via Self-Learned Bottleneck Neural Representation
Authors: Haofeng Huang, Wenhan Yang, Lingyu Duan, Jiaying Liu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the robustness and superior effectiveness of our proposed method. Our project is publicly available at: https://huangerbai.github.io/SLBNR/. We provide quantitative results in Table 1 and Table 2. We conduct ablation studies as shown in Table 3. |
| Researcher Affiliation | Academia | 1Peking University, Beijing, China, 2Peng Cheng Laboratory, Beijing, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Our project is publicly available at: https://huangerbai.github.io/SLBNR/. |
| Open Datasets | Yes | The evaluation dataset is commonly used DRV (Chen et al. 2019) which provides dynamic videos of a real dark scene. |
| Dataset Splits | No | The paper describes how an 'evaluation set' was chosen but does not specify a separate 'validation' dataset split with percentages, counts, or a detailed splitting methodology for reproducibility of model training. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | The training starts with a 300-epochs self-regression then continue with a fully-equipped loss for another 300 epochs. We choose λ1=100, λ2=10 4, λ3=10 3, λ4=1, λ5=1. |