Generalised Implicit Neural Representations
Authors: Daniele Grattarola, Pierre Vandergheynst
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experiments with our method on various real-world signals on non-Euclidean domains. Results In the experiments section, we show concrete examples of learning INRs for signals on graphs and manifolds, using real-world data from biology and social networks. |
| Researcher Affiliation | Academia | Daniele Grattarola EPFL Lausanne, Switzerland daniele.grattarola@epfl.ch Pierre Vandergheynst EPFL Lausanne, Switzerland pierre.vandergheynst@epfl.ch |
| Pseudocode | No | The paper describes its method in prose and equations, but does not contain a structured pseudocode or algorithm block. |
| Open Source Code | Yes | The code to reproduce our results and the high-resolution version of all figures are available at https://github.com/danielegrattarola/GINR. |
| Open Datasets | Yes | Bunny We generate a texture on the Stanford bunny mesh1 using the Gray-Scott reaction-diffusion model... 1Available at https://graphics.stanford.edu/data/3Dscanrep/ Protein As a real-world domain, we consider the solvent excluded surface of a protein structure.3 The continuous signal is the value of the electrostatic field generated by the amino acid residues at the surface. ...3Protein Data Bank identifier: 1AA7... Data We collected data from the National Oceanic and Atmospheric Administration (NOAA) Operational Model Archive and Distribution System, specifically from the Global Forecast System (GFS). |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] At the beginning of Section 4 and in Section 4.1. We report in Tab. 2 the R2 for a held-out set of nodes (to evaluate whether the INRs are overfitting instead of learning a meaningful representation). |
| Hardware Specification | Yes | We ran all experiments on an Nvidia Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions 'SIREN multi-layer perceptron' and 'Adam' as software components, but does not provide specific version numbers for these or any other libraries or frameworks. |
| Experiment Setup | Yes | Setting We implement the generalised INR as a SIREN multi-layer perceptron [45]. The model has 6 layers with 512 hidden neurons and a skip connection from the input to the middle layer. We use the same hyperparameters and initialisation scheme suggested by Sitzmann et al. [45]. We train the model using Adam [27] with a learning rate of 10 4 and an annealing schedule that halves the learning rate if the loss does not improve for 1000 steps. At each step, we sample 5000 nodes randomly from the graph as a mini-batch. We use spectral embeddings of size k = 100... |