🤖 AI Summary
This work addresses the challenge of rotation-invariant object recognition under data-scarce conditions by proposing a compact deep network architecture that integrates a spectral-spatial polar representation. The method uniquely embeds a spectral-spatial polar structure into a lightweight neural network, mathematically guaranteeing strict rotational invariance of features without relying on data augmentation. Experimental results demonstrate that, in low-data regimes, the proposed model significantly outperforms conventional convolutional neural networks and achieves theoretically provable rotation-invariant recognition performance.
📝 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