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..
DFVSR: Directional Frequency Video Super-Resolution via Asymmetric and Enhancement Alignment Network
Authors: Shuting Dong, Feng Lu, Zhe Wu, Chun Yuan
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments, 4.1 Datasets and Implementation, 4.2 Comparison with State-of-The-Art Methods, 4.3 Ablation Study, Table 1: Quantitative comparison (PSNR and SSIM) of different methods on REDS4, Vimeo-T, Vid4 and UDM10 with upscale factor 4 under BI and BD degradations. |
| Researcher Affiliation | Collaboration | Shuting Dong1,2 , Feng Lu1,2 , Zhe Wu2 and Chun Yuan1,2 1Tsinghua Shenzhen International Graduate School, Tsinghua University 2Peng Cheng Laboratory |
| Pseudocode | No | The paper describes its proposed network and modules using textual descriptions and architectural diagrams (Figure 1 and Figure 2), but it does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code for the methodology or a link to a code repository. |
| Open Datasets | Yes | We adopt two widely used datasets to train: REDS [Nah et al., 2019] and Vimeo-90K [Xue et al., 2019]. |
| Dataset Splits | Yes | Following [Chan et al., 2021a], we apply REDS4 as our test set, and REDSval4 as the validation set. |
| Hardware Specification | Yes | The model is trained under the Py Torch framework with an NVIDIA RTX 2080Ti GPU. |
| Software Dependencies | No | The paper mentions training under the 'PyTorch framework' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We employ Adam optimizer by setting β1 = 0.9 and β2 = 0.999. The learning rate is initialized as 2.5 10 5. We apply RGB patches of size 64 64 as inputs. We set the mini-batch size to 32. In addition to our proposed DFLoss, we also adopt Charbonnier loss [Lai et al., 2017], and ε is set to 1 10 3. The total number of iterations is 600K. |