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..
Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions
Authors: Jeremy Ocampo, Matthew Alexander Price, Jason McEwen
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the DISCO spherical CNN framework to a number of benchmark dense-prediction problems on the sphere, such as semantic segmentation and depth estimation, on all of which we achieve the state-of-the-art performance. |
| Researcher Affiliation | Collaboration | Jeremy Ocampo1,2, Matthew A. Price1,2, Jason D. Mc Ewen1,2 1Kagenova Limited, 2University College London (UCL) |
| Pseudocode | Yes | Algorithm 1 Function to compute custom sparse gradients in Tensor Flow. |
| Open Source Code | No | Section 5 mentions "implemented in the Copernic AI 3 code" with a footnote link to "https://www.kagenova.com/products/copernicAI/", which is a product page and not a direct open-source code repository for the methodology described in the paper. |
| Open Datasets | Yes | We project the MNIST digits onto the sphere at resolution L = 1024, using the same projection as in Cohen et al. (2018).The 2D3DS dataset (Armeni et al., 2017) consists of 1413 equirectangular RGB-Depth indoor 360 images...The Omni-SYNTHIA dataset (Ros et al., 2016) consists of 2269 panoramic RGB images...The Matterport3D dataset (Chang et al., 2017) contains 7907 spherical RGB images... |
| Dataset Splits | Yes | We use the same 3-fold split for cross-validation as in Jiang et al. (2019).We use the same train/test/validation split as in Albanis et al. (2021). |
| Hardware Specification | Yes | On an NVIDIA RTX 3090 GPU we observe a wall-clock compute time of 0.0302 0.0018, 0.0898 0.0025, and 0.3255 0.0043 seconds for resolutions of L = 1024, L = 2048, and L = 4096, respectively, when averaged over 10 experiments. |
| Software Dependencies | No | The paper mentions the use of TensorFlow, PyTorch, ADAM optimizer (Kingma & Ba, 2015), and Group Normalization (Wu & He, 2018), but does not specify version numbers for any of these software dependencies. |
| Experiment Setup | Yes | We train for 10 epochs usign the ADAM optimizer (Kingma & Ba, 2015), with a learning rate of 0.001 and a batch size of 8. |