A sampling theory perspective on activations for implicit neural representations
Authors: Hemanth Saratchandran, Sameera Ramasinghe, Violetta Shevchenko, Alexander Long, Simon Lucey
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we aim to compare the performance of different INR activations. First, we focus on image and Ne RF reconstructions and later move on to dynamical systems. |
| Researcher Affiliation | Collaboration | 1University of Adelaide, Australia 2Amazon, Australia. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | DIV2K dataset (Agustsson & Timofte, 2017) |
| Dataset Splits | No | The paper mentions training and testing but does not provide specific details on dataset splits (e.g., percentages or exact counts for train/validation/test). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We use 4-layer networks with 256 width for these experiments. |