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..
NeRV: Neural Representations for Videos
Authors: Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser Nam Lim, Abhinav Shrivastava
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments on Big Buck Bunny sequence from scikit-video to compare our Ne RV with pixel-wise implicit representations, which has 132 frames of 720 1080 resolution. To compare with state-of-the-arts methods on video compression task, we do experiments on the widely used UVG [7], consisting of 7 videos and 3900 frames with 1920 1080 in total. |
| Researcher Affiliation | Collaboration | 1University of Maryland, College Park, 2Facebook AI |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. Figure 3 illustrates a pipeline, but it is a diagram, not structured pseudocode. |
| Open Source Code | Yes | The source code and pre-trained model can be found at https://github.com/haochen-rye/Ne RV.git. |
| Open Datasets | Yes | We perform experiments on Big Buck Bunny sequence from scikit-video to compare our Ne RV with pixel-wise implicit representations, which has 132 frames of 720 1080 resolution. To compare with state-of-the-arts methods on video compression task, we do experiments on the widely used UVG [7], consisting of 7 videos and 3900 frames with 1920 1080 in total. |
| Dataset Splits | No | No explicit information on training/validation/test dataset splits, such as percentages or sample counts, was provided in the paper. The paper mentions 'training epochs' and 'batchsize' but does not define a separate validation set split. |
| Hardware Specification | Yes | All experiments are run with NVIDIA RTX2080ti. |
| Software Dependencies | No | The paper mentions 'Py Torch [54]' but does not provide a specific version number. Other mentioned tools like 'Adam optimizer [51]' and 'cosine annealing learning rate schedule [52]' are algorithms or schedules, not software dependencies with version numbers. |
| Experiment Setup | Yes | In our experiments, we train the network using Adam optimizer [51] with learning rate of 5e-4. For ablation study on UVG, we use cosine annealing learning rate schedule [52], batchsize of 1, training epochs of 150, and warmup epochs of 30 unless otherwise denoted. When compare with state-of-the-arts, we run the model for 1500 epochs, with batchsize of 6. For experiments on Big Buck Bunny , we train Ne RV for 1200 epochs unless otherwise denoted. For ο¬ne-tune process after pruning, we use 50 epochs for both UVG and Big Buck Bunny . |