Many settings in machine learning require the selection of a rotation representation. However, choosing a suitable representation from the many available options is challenging. This paper acts as a survey and guide through rotation representations. We walk through their properties that harm or benefit deep learning with gradient-based optimization. By consolidating insights from rotation-based learning, we provide a comprehensive overview of learning functions with rotation representations. We provide guidance on selecting representations based on whether rotations are in the model's input or output and whether the data primarily comprises small angles.

翻译：在机器学习中，许多场景需要选择合适的旋转表示方法。然而，从众多可用选项中选择合适的表示方法具有挑战性。本文旨在系统梳理旋转表示方法并为其选择提供指导。我们深入分析了不同表示方法在基于梯度优化的深度学习中的优缺点。通过整合旋转学习领域的研究成果，我们对基于旋转表示的函数学习方法进行了全面综述。我们针对旋转位于模型输入或输出端、以及数据是否主要包含小角度等情况，提供了具体的表示方法选择建议。