Efficient Generalized Spherical CNNs
Authors: Oliver Cobb, Christopher G. R. Wallis, Augustine N. Mavor-Parker, Augustin Marignier, Matthew A. Price, Mayeul d'Avezac, Jason McEwen
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate state-of-the-art performance on all spherical benchmark problems considered, both in terms of accuracy and parameter efficiency. |
| Researcher Affiliation | Industry | Oliver J. Cobb, Christopher G. R. Wallis, Augustine N. Mavor-Parker, Augustin Marignier, Matthew A. Price, Mayeul d Avezac & Jason D. Mc Ewen Kagenova Limited, Guildford GU5 9LD, UK |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'fourpi AI 1 code' which links to a commercial product page (https://www.kagenova.com/products/fourpi AI/) and a 'private tensossht 4 code' that is 'Available on request from https://www.kagenova.com/', neither of which constitute publicly available open-source code for the described methodology. |
| Open Datasets | Yes | We consider the now standard benchmark problem of classifying MNIST digits projected onto the sphere. ... using the QM7 dataset (Blum & Reymond, 2009; Rupp et al., 2012). ... the SHREC 17 (Savva et al., 2017) competition dataset, containing 51k 3D object meshes. |
| Dataset Splits | No | For the spherical component we again adopt the convolutional architecture shown in Figure 4 except with one fewer efficient generalized layer. We use bandlimits of p L0, L1, L2, L3, L4q p10, 6, 6, 3, 1q, channels of p K0, K1, K2, K3, K4q p5, 16, 24, 32, 40q and τmax 6. One minor difference is that this time we include a skip connection between the ℓ 0 components of the fourth and fifth layer. We proceed the convolutional layers with two dense layers of size p256, 64q and use batch normalization between each layer. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, or memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'ssht' and 'so3' software packages, and a 'Tensor Flow implementation', but it does not specify version numbers for these software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | We train the network for 50 epochs on batches of size 32, using the Adam optimizer (Kingma & Ba, 2015) with a decaying learning rate starting at 0.001. For the restricted generalized convolutions we follow the approach of Kondor et al. (2018) by using L1 regularization (regularization strength 10 5)... |