Gauge Equivariant Convolutional Networks and the Icosahedral CNN
Authors: Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on Ico MNIST, climate pattern segmentation, and omnidirectional RGB-D image segmentation are presented in Sec. 5. |
| Researcher Affiliation | Collaboration | 1Qualcomm AI Research, Amsterdam, NL. 2Qualcomm-University of Amsterdam (QUVA) Lab. |
| Pseudocode | No | The paper describes algorithms but does not provide structured pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper does not provide a statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | We use the exact same data and evaluation methodology as (Jiang et al., 2018). The preprocessed data as released by (Jiang et al., 2018) consists of 16-channel spherical images at resolution r = 5, which we reinterpret as icosahedral signals (introducing slight distortion). See (Mudigonda et al., 2017) for a detailed description of the data. |
| Dataset Splits | Yes | We generate three different versions of the training and test sets, differing in the transformations applied to the data. |
| Hardware Specification | No | The paper mentions experiments were run but does not specify any hardware details such as GPU/CPU models or types. |
| Software Dependencies | No | The paper mentions 'conv2d' and deep learning primitives but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | Our main model consists of one gauge equivariant scalar-to-regular (S2R) convolution layer, followed by 6 regular-to-regular (R2R) layers and 3 FC layers (see Supp. Mat. for architectural details). We also evaluate a model that uses only S2R convolution layers, followed by orientation pooling (a max over the 6 orientation channels of each regular feature, thus mapping a regular field to a scalar), as in (Masci et al., 2015). Finally, we consider a model that uses only rotation-invariant filters, i.e. scalar-to-scalar (S2S) convolutions, similar to standard graph CNNs (Boscaini et al., 2015; Kipf & Welling, 2017). We also compare to the fully SO(3)-equivariant but computationally costly Spherical CNN (S2CNN). See supp. mat. for architectural details and computational complexity analysis. |