Group equivariant convolutional neural networks have been designed for a variety of geometric transformations from 2D and 3D rotation groups, to semi-groups such as scale. Despite the improved interpretability, accuracy and generalizability afforded by these architectures, group equivariant networks have seen limited application in the context of perceptual quantities such as hue and saturation, even though their variation can lead to significant reductions in classification performance. In this paper, we introduce convolutional neural networks equivariant to variations in hue and saturation by design. To achieve this, we leverage the observation that hue and saturation transformations can be identified with the 2D rotation and 1D translation groups respectively. Our hue-, saturation-, and fully color-equivariant networks achieve equivariance to these perceptual transformations without an increase in network parameters. We demonstrate the utility of our networks on synthetic and real world datasets where color and lighting variations are commonplace.

我们设计了对色调和饱和度变化等感知转换具有等变性的卷积神经网络，通过观察到色调和饱和度变换可以分别与2D旋转和1D平移群相对应，实现了对这些感知变换的等变性，同时不增加网络参数。我们在合成和真实世界数据集上展示了我们的网络的实用性，其中颜色和光照的变化很常见。

颜色均变网络