A General Theory of Equivariant CNNs on Homogeneous Spaces
Authors: Taco S. Cohen, Mario Geiger, Maurice Weiler
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a general theory of Group equivariant Convolutional Neural Networks (G-CNNs) on homogeneous spaces such as Euclidean space and the sphere... This paper does not contain truly new mathematics (in the sense that a professional mathematician with expertise in the relevant subjects would not be surprised by our results), but instead provides a new formalism for the study of equivariant convolutional networks. |
| Researcher Affiliation | Collaboration | Taco S. Cohen Qualcomm AI Research Qualcomm Technologies Netherlands B.V. tacos@qti.qualcomm.com Mario Geiger PCSL Research Group EPFL mario.geiger@epfl.ch Maurice Weiler QUVA Lab U. of Amsterdam m.weiler@uva.nl |
| Pseudocode | No | The paper focuses on theoretical derivations and concepts, and does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper discusses implementation aspects generally and references other works ([7], [10]), but does not provide a specific link or explicit statement for the open-sourcing of code developed for the theory presented in this paper. |
| Open Datasets | No | This is a theoretical paper and does not describe any experiments or datasets, thus no information about public datasets for training is provided. |
| Dataset Splits | No | This is a theoretical paper and does not describe any experiments, therefore no specific dataset split information for validation is provided. |
| Hardware Specification | No | This is a theoretical paper and does not describe any experiments, therefore no specific hardware details are provided. |
| Software Dependencies | No | This is a theoretical paper and does not describe any implementations with specific software dependencies or version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experiments or their setup, including hyperparameters or system-level training settings. |