S2P-Net is a deep learning model that builds rotation invariance directly into its spectral-spatial polar design rather than learning it from augmented data.
Delving deep into rectifiers: Surpassing human-level performance on
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S2P-Net: A Spectral-Spatial Polar Network for Rotation-Invariant Object Recognition in Low-Data Regimes
S2P-Net is a deep learning model that builds rotation invariance directly into its spectral-spatial polar design rather than learning it from augmented data.