S2P-Net: A Spectral-Spatial Polar Network for Rotation-Invariant Object Recognition in Low-Data Regimes
arXiv:2605.0966716.6
AI Analysis
Provides a compact rotation-invariant architecture for object recognition in data-scarce scenarios.
S2P-Net achieves mathematically guaranteed rotation invariance for object recognition without data augmentation, outperforming CNNs in low-data regimes.
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