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 [1].
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. |