Learning Spatially Collaged Fourier Bases for Implicit Neural Representation
Authors: Jason Chun Lok Li, Chang Liu, Binxiao Huang, Ngai Wong
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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 {jasonlcl, lcon7, huangbx7}@connect.hku.hk, nwong@eee.hku.hk |
| 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. |