Many successful deep learning architectures are equivariant to certain
transformations in order to conserve parameters and improve generalization:
most famously, convolution layers are equivariant to shifts of the input. This
approach only works when practitioners know the symmetries of the task and can
manually construct an architecture with the corresponding equivariances. Our
goal is an approach for learning equivariances from data, without needing to
design custom task-specific architectures. We present a method for learning and
encoding equivariances into networks by learning corresponding parameter
sharing patterns from data. Our method can provably represent
equivariance-inducing parameter sharing for any finite group of symmetry
transformations. Our experiments suggest that it can automatically learn to
encode equivariances to common transformations used in image processing tasks.
We provide our experiment code at
this https URL

这篇研究论文介绍了一种从数据学习平移对称性、提高深度学习在图像处理任务中的性能的方法，而非人为设计具有相应等变性质的架构，其通过学习相应的参数共享模式来实现对等变性的学习及编码，并且结果显示其成功替代了传统手动构建深度学习架构的方法。