You are all cordially invited to the AMLab seminar on **Tuesday March 14** at 16:00 in C3.163, where **Taco Cohen** will give a talk titled “**Group Equivariant & Steerable CNNs**”. Afterwards there are the usual drinks and snacks!

**Abstract**: Deep learning can be very effective, but typically requires large amounts of labelled data, which can be costly to collect. This is not only a major practical limitation to the applicability of deep learning, but also a fundamental barrier to AI: rapid learning is an essential part of intelligence.

In this talk I will present *group equivariant networks*, a natural generalization of convolutional networks that achieves improved statistical efficiency by exploiting symmetries like rotation and reflection. Instead of using convolutions, these networks use group equivariant convolutions. Group equivariant convolutions are easy to use, fast, and can be converted to standard convolutions after training. We show that simply replacing translational convolutions with group equivariant convolutions can improve image classification results. In the second part of the talk I will show how group equivariant nets can be scaled up to very large symmetry groups using steerable filters.