Abstract
Spatial Transformer layers allow neural networks, at least in principle, to be invariant to large spatial transformations in image data. The model has, however, seen limited uptake as most practical implementations support only transformations that are too restricted, e.g. affine or homographic maps, and/or destructive maps, such as thin plate splines. We investigate the use of flexible diffeomorphic image transformations within such networks and demonstrate that significant performance gains can be attained over currently-used models. The learned transformations are found to be both simple and intuitive, thereby providing insights into individual problem domains. With the proposed framework, a standard convolutional neural network matches state-of-the-art results on face verification with only two extra lines of simple TensorFlow code.
Original language | English |
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Title of host publication | Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition |
Publisher | IEEE |
Publication date | 2018 |
Pages | 4403-4412 |
ISBN (Electronic) | 978-1-5386-6420-9 |
DOIs | |
Publication status | Published - 2018 |
Event | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition - Salt Lake City, United States Duration: 18 Jun 2018 → 23 Jun 2018 https://ieeexplore.ieee.org/xpl/conhome/8576498/proceeding?isnumber=8578098&refinementName=Author |
Conference
Conference | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition |
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Country/Territory | United States |
City | Salt Lake City |
Period | 18/06/2018 → 23/06/2018 |
Internet address |
Series | I E E E Conference on Computer Vision and Pattern Recognition. Proceedings |
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ISSN | 1063-6919 |