http://arxiv.org/abs/2111.04742
In this work we introduce group-equivariant self-attention models to address the problem of explainable radio galaxy classification in astronomy. We evaluate various orders of both cyclic and dihedral equivariance, and show that including equivariance as a prior both reduces the number of epochs required to fit the data and results in improved performance. We highlight the benefits of equivariance when using self-attention as an explainable model and illustrate how equivariant models statistically attend the same features in their classifications as human astronomers.
M. Bowles, M. Bromley, M. Allen, et. al.
Wed, 10 Nov 21
25/63
Comments: 7 pages, 3 figures, NeurIPS, Workshop: Machine Learning and the Physical Sciences