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QUAN: Quaternion Approximate Networks Improve Image Classification and Oriented Object Detection

The paper proposes Quaternion Approximate Networks (QUAN), which approximate quaternion convolution through Hamilton product decomposition using real-valued operations, paired with custom CUDA kernels to achieve rotation-equivariant image classification and object detection. The authors also propose Independent Quaternion Batch Normalization (IQBN) to stabilize training and extend quaternion operations to spatial attention mechanisms.

Part of the event: QUAN: Quaternion Approximation Networks for Rotation-Equivariant Vision

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