Quaternion Product Units for Deep Learning on 3D Rotation Groups Xuan Zhang1, Shaofei Qin1, Yi Xu∗1, and Hongteng Xu2 1MoE Key Lab of Artificial Intelligence, AI Institute 2Infinia ML, Inc. 1Shanghai Jiao Tong University, Shanghai, China 2Duke University, Durham, NC, USA ffloatlazer, 1105042987,
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[email protected] Abstract methods have made efforts to enhance their robustness to rotations [4,6, 29,3, 26]. However, the architectures of We propose a novel quaternion product unit (QPU) to their models are tailored for the data in the Euclidean space, represent data on 3D rotation groups. The QPU lever- rather than 3D rotation data. In particular, the 3D rotation ages quaternion algebra and the law of 3D rotation group, data are not closed under the algebraic operations used in representing 3D rotation data as quaternions and merg- their models (i:e:, additions and multiplications).1 The mis- ing them via a weighted chain of Hamilton products. We matching between data and model makes these methods dif- prove that the representations derived by the proposed QPU ficult to analyze the influence of rotations on their outputs can be disentangled into “rotation-invariant” features and quantitatively. Although this mismatching problem can be “rotation-equivariant” features, respectively, which sup- mitigated by augmenting training data with additional rota- ports the rationality and the efficiency of the QPU in the- tions [18], this solution gives us no theoretical guarantee. ory. We design quaternion neural networks based on our To overcome the challenge mentioned above, we pro- QPUs and make our models compatible with existing deep posed a novel quaternion product unit (QPU) for 3D ro- learning models.