General Nonlinearities in SO(2)-Equivariant CNNs
Authors: Daniel Franzen, Michael Wand
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments with 2D and 3D data, we obtain results that compare favorably to the state-of-the-art in terms of accuracy while permitting continuous symmetry and exact equivariance. |
| Researcher Affiliation | Academia | Daniel Franzen Institute of Computer Science Johannes Gutenberg University Mainz Staudingerweg 9, 55122 Mainz, Germany dfranz@uni-mainz.de Michael Wand Institute of Computer Science Johannes Gutenberg University Mainz Staudingerweg 9, 55122 Mainz, Germany wandm@uni-mainz.de |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | A more comprehensive presentation, along with source code, is provided in the supplementary material. |
| Open Datasets | Yes | We test our implementation on image and 3D data. In the image case, we replicate the architecture used by Weiler and Cesa [39] on the MNIST-rot dataset from their recent survey of E(2)-equivariant image processing networks. ... For the 3D surfel case, we use Model Net-40 [44] as benchmark. |
| Dataset Splits | No | The paper mentions training models with a batch size but does not provide specific details on validation data splits (percentages, counts, or explicit methodology for splitting). |
| Hardware Specification | Yes | In Table 1, we list training time in seconds per epoch on a single Nvidia RTX 2080 Ti graphics card. |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'Py Ke Ops [2]' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Architecture: Our concrete network design follows the construction of Weiler and Cesa [39] for their best MNIST-rot models. We use the same number of equivariant and linear layers with the same output channel count and filter properties (radii, rotation orders and width) and also apply Dropout [33] with p = 0.3 before each linear layer. We train our models for 40 epochs with a batch size of 64 images. We use the Adam [21] optimizer, starting with a learning rate of 0.015, with an exponential decay factor of 0.8 per epoch starting after epoch 16. |