| Speaker(s) | Matthias Ehrhardt University of Bath |
| Date | 29 September 2021 – 11:00 to 11:40 |
| Venue | INI Seminar Room 1 |
| Session Title | Equivariant Neural Networks for Inverse Problems |
| Chair | Tatiana Alessandra Bubba |
| Event | [MDLW02] Deep learning and inverse problems |
| Abstract | In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of this approach have motivated a line of research into incorporating other symmetries into deep learning methods, in the form of group equivariant convolutional neural networks. In this work, we demonstrate that roto-translational equivariant convolutions can improve reconstruction quality compared to standard convolutions when used within a learned reconstruction method. This is almost a free lunch since only little extra computational cost during training and absolutely no extra cost at test time is needed. |
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