Abstract: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
| Comments: | 9 pages, 4 figures, 3 tables. Preprint. Code available from the author upon request |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.09667 [cs.CV] |
| (or arXiv:2605.09667v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.09667 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.5281/zenodo.19744626
DOI(s) linking to related resources |
Submission history
From: Albert Heruth [view email]
[v1]
Sun, 10 May 2026 17:31:27 UTC (306 KB)