mlsanityData to 14 Oct 2026

Data Augmentation and Regularization for Learning Group Equivariance

arXiv 2502.06547 ↗First posted 10 Feb 2025stat.ML cs.LG math.OCcited by 3

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Abstract (arXiv)

In many machine learning tasks, known symmetries can be used as an inductive bias to improve model performance. In this paper, we consider learning group equivariance through training with data augmentation. We summarize results from a previous paper of our own, and extend the results to show that equivariance of the trained model can be achieved through training on augmented data in tandem with regularization.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 18 references that are in the map. First posted on arXiv 10 Feb 2025.

Builds on

14 of 18 references.

References · 14

  1. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
  2. 2016Group Equivariant Convolutional Networkscited by 1,165
  3. 2018Invariant and Equivariant Graph Networkscited by 346
  4. 2016Steerable CNNscited by 305
  5. 2018A General Theory of Equivariant CNNs on Homogeneous Spacescited by 228
  6. 2021A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groupscited by 187
  7. 2021Provably Strict Generalisation Benefit for Equivariant Modelscited by 91
  8. 2020On the Benefits of Invariance in Neural Networkscited by 79
  9. 2021Implicit Bias of Linear Equivariant Networkscited by 19
  10. 2023Optimization Dynamics of Equivariant and Augmented Neural Networkscited by 18
  11. 2024Emergent Equivariance in Deep Ensemblescited by 18
  12. 2024Ensembles provably learn equivariance through data augmentationcited by 8
2 more
  1. 2023On the Implicit Bias of Linear Equivariant Steerable Networkscited by 7
  2. 2024Symmetries in Overparametrized Neural Networks: A Mean-Field Viewcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026An analytic theory of convolutional neural network inverse problems solverscited by 2
  2. 2026Conservation Laws from Data Symmetry in Neural Networkscited by 1
  3. 2026Equivariance and Augmentation for Bayesian Neural Networkscited by 0