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..
Learning Spatially Collaged Fourier Bases for Implicit Neural Representation
Authors: Jason Chun Lok Li, Chang Liu, Binxiao Huang, Ngai Wong
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments demonstrate the superior reconstruction quality of the proposed approach over existing baselines across various INR tasks, including image fitting, video representation, and 3D shape representation. |
| Researcher Affiliation | Academia | Department of Electrical and Electronic Engineering, The University of Hong Kong EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions that baselines are 'based on official codes released by authors of the respective models' but does not provide a statement or link for the open-sourcing of SCONE's code. |
| Open Datasets | Yes | The image representation task is performed on selected images from the Kodak dataset (Eastman Kodak Company 1999)... For the 3D shape representation task, we use the Stanford 3D scan dataset. |
| Dataset Splits | No | The paper describes sampling data for training but does not provide specific details on train/validation/test dataset splits, percentages, or explicit counts needed for reproduction. |
| Hardware Specification | Yes | All models are trained for 10k iterations on Nvidia RTX 3090 GPUs, each equipped with a 24GB memory buffer. |
| Software Dependencies | No | The implementation of all codes is carried out using the Py Torch (Paszke et al. 2019) framework, but no specific version number for PyTorch or other software dependencies is provided. |
| Experiment Setup | Yes | All models are trained for 10k iterations on Nvidia RTX 3090 GPUs, each equipped with a 24GB memory buffer. The training process utilizes the Adam optimizer (Kingma and Ba 2014), with the parameters β1 = 0.9 and β2 = 0.999, and employs the Mean Squared Error (MSE) loss without weight decay. Additionally, a cosine learning rate scheduler is applied, with a minimum learning rate of 1e 6. |