Achieving Rotation-Invariant Convolution via Non-Learnable Orientation Alignment Operators

ArXi:2404.11309v2 Announce Type: replace Achieving rotational invariance in deep neural networks without data augmentation is a research hotspot. Intrinsic invariance enables features to capture targets' inherent properties, enhancing deep learning performance in visual tasks. Based on various types of non-learnable operators, this paper proposes a comprehensive set of convolution operations that are natually invariant to arbitrary rotations.