As I understand STN as described by the the deepmind paper https://arxiv.org/abs/1506.02025
- allow a neural network to learn how to perform spatial transformations on the input image in order to enhance the geometric invariance of the model.
whereas Deformable Convolutions https://arxiv.org/abs/1703.06211
- add 2D offsets to the regular grid sampling locations in the standard convolution.
From the paper:
...deformable convolution does not adopt a global parametric transformation and feature warping. Instead, it samples the feature map in a local and dense manner. To generate new feature maps, it has a weighted summation step, which is absent in STN.
To me STNs augment the input image whereas DC augment the kernel shape. Results should be similar/same in terms of field of view of the network? Please correct me if mistaken.