Reviews: Chirality Nets for Human Pose Regression

A growing body of literature has shown that building symmetries into neural networks through equivariant layers is an effective means of improving results, especially in the face of limited data and even when data augmentation is used. This paper continues that trend by showing that equivariance to chirality transformations consistently improves results on pose regression tasks. The paper is well written and easy to follow. Related work is discussed in a mostly adequate and balanced manner. The work fits in existing theoretical frameworks when considering that the group acts in a linear way (though not via permutations).