$\texttt{NAISR}$: A 3D Neural Additive Model for Interpretable Shape Representation
Authors: Yining Jiao, Carlton Jude ZDANSKI, Julia S Kimbell, Andrew Prince, Cameron P Worden, Samuel Kirse, Christopher Rutter, Benjamin Shields, William Alexander Dunn, Jisan Mahmud, Marc Niethammer
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate NAISR with respect to shape reconstruction, shape disentanglement, shape evolution, and shape transfer on three datasets: 1) Starman, a simulated 2D shape dataset; 2) the ADNI hippocampus 3D shape dataset; and 3) a pediatric airway 3D shape dataset. Our experiments demonstrate that NAISR achieves excellent shape reconstruction performance while retaining interpretability. |
| Researcher Affiliation | Academia | 1University of North Carolina at Chapel Hill, 2Wake Forest School of Medicine, 3The Ohio State University College of Medicine |
| Pseudocode | No | The paper includes mathematical formulations and figures but no explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available. ... We have submitted our source code. The implementation of NAISR will be made publicly available. |
| Open Datasets | Yes | Data used in preparation of this article were obtained from the Alzheimer s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). ... Starman, a simulated 2D shape dataset (Bône et al., 2020). |
| Dataset Splits | No | The paper mentions "80%-20% train-test split" for datasets but does not explicitly describe a separate validation split or how validation data was partitioned or used. |
| Hardware Specification | Yes | The model is trained on an Nvidia Ge Force RTX 3090 GPU for approximately 12 hours for 3000 epochs for the airway dataset. |
| Software Dependencies | No | The paper mentions using "Adam (Kingma & Ba, 2014)" and "SIREN (Sitzmann et al., 2020)" but does not specify version numbers for these or other software components. |
| Experiment Setup | Yes | There are 256 hidden units in each layer. ... We train NAISR for 3000 epochs for the airway dataset and 300 epochs for the ADNI hippocampus and Starman datasets using Adam (Kingma & Ba, 2014) with a learning rate 5e 5 and batch size of 64. ... During training, λ1 = λ5 = 1 10; λ2 = 3 10; λ3 = 1 10, λ4 = 1 102. For Llat, λ6 = 2 L; σ = 0.01 ... During inference, the latent codes are optimized for Nt iterations with a learning rate of 5e 3. Nt is set to 800 for the pediatric airway dataset; Nt is set to 200 for the Starman and ADNI Hippocampus datasets. |