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S2P-Net: A Spectral-Spatial Polar Network for Rotation-Invariant Object Recognition in Low-Data Regimes

arXiv 2605.09667 ↗First posted 10 May 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

We present S2P-Net (Spectral-Spatial Polar Network), a compact deep learning architecture that achieves mathematically guaranteed rotation invariance without data augmentation. In this Paper, we also made a comparison to other neural network architectures (CNN`s). Have a look at the results and feel free to contact me for any questions. This is my first paper:) Made by Hackbert

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 10 May 2026.

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14 of 19 references.

References · 14

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  2. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  3. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  4. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  5. 2016Matching Networks for One Shot Learningcited by 2,246
  6. 2016Group Equivariant Convolutional Networkscited by 1,154
  7. 2019General E(2)-Equivariant Steerable CNNscited by 441
  8. 2016Harmonic Networks: Deep Translation and Rotation Equivariancecited by 293
  9. 2020Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Datacited by 248
  10. 2017Learning Steerable Filters for Rotation Equivariant CNNscited by 193
  11. 2019Scale-Equivariant Steerable Networkscited by 110
  12. 2016Rotation equivariant vector field networkscited by 98
2 more
  1. 2016Exploiting Cyclic Symmetry in Convolutional Neural Networkscited by 91
  2. 2017Polar Transformer Networkscited by 81

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