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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Group Equivariant Stand-Alone Self-Attention For Vision
Authors: David W. Romero, Jean-Baptiste Cordonnier
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on vision benchmarks demonstrate consistent improvements of GSA-Nets over non-equivariant self-attention networks. |
| Researcher Affiliation | Academia | David W. Romero Vrije Universiteit Amsterdam EMAIL Jean-Baptiste Cordonnier Ecole Polytechnique F ed erale de Lausanne (EPFL) EMAIL |
| Pseudocode | No | The paper presents mathematical formulations and conceptual diagrams (Fig. B.1, Fig. B.2) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/dwromero/g selfatt. |
| Open Datasets | Yes | Rot MNIST. The rotated MNIST dataset (Larochelle et al., 2007)...CIFAR-10. The CIFAR-10 dataset (Krizhevsky et al., 2009)...PCam. The Patch Camelyon dataset (Veeling et al., 2018) |
| Dataset Splits | Yes | Rot MNIST...divided into training, validation and test sets of 10k, 2k and 50k images...CIFAR-10...divided into training, validation and test sets of 40k, 10k and 10k images. |
| Hardware Specification | No | The paper mentions GPU usage in Table 1 (e.g., '1GPU', '2GPU'), but it does not specify concrete hardware details like GPU models (e.g., NVIDIA A100, RTX 2080 Ti) or CPU types. |
| Software Dependencies | No | The paper states 'We utilize Py Torch for our implementation' but does not provide specific version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | For rotational MNIST...We train for 300 epochs and utilize the Adam optimizer, batch size of 8, weight decay of 0.0001 and learning rate of 0.001. |