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 [1].
General Nonlinearities in SO(2)-Equivariant CNNs
Authors: Daniel Franzen, Michael Wand
NeurIPS 2021 | Venue PDF | 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 EMAIL Michael Wand Institute of Computer Science Johannes Gutenberg University Mainz Staudingerweg 9, 55122 Mainz, Germany EMAIL |
| 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 ο¬lter 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. |