Towards Croppable Implicit Neural Representations
Authors: Maor Ashkenazi, Eran Treister
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on various signal encoding tasks and compare it to alternatives. |
| Researcher Affiliation | Academia | Maor Ashkenazi Ben-Gurion University of the Negev maorash@post.bgu.ac.il Eran Treister Ben-Gurion University of the Negev erant@cs.bgu.ac.il |
| Pseudocode | Yes | Algorithm 1 Automatic Partitioning |
| Open Source Code | Yes | Code is available at https://github.com/maorash/Local-Global-INRs. |
| Open Datasets | Yes | Next, from the DIV2K dataset [2], we have randomly selected a subset of 25 images which were downsampled by a factor of four before training. |
| Dataset Splits | No | The paper discusses training and testing but does not explicitly describe the use of a validation set or its split ratios for hyperparameter tuning or model selection. |
| Hardware Specification | Yes | We ran the experiments multiple times on a single Nvidia RTX3090. |
| Software Dependencies | No | The paper mentions using 'Adam W scheduler [26]' and 'Adam optimizer [22]', but does not specify version numbers for general software dependencies or libraries (e.g., PyTorch version, Python version). |
| Experiment Setup | Yes | The full configuration and networks size for all experiments is in Appendix B. |