Spatial Transformer Networks vs Deformable Convolutions 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.
The main difference, as they mentioned in the paper, is that STN has a global parameter to transform the features. That is, it computes one set of parameters to transform the input. DC computes a 2D offset map for each location in the input, so for each location in the input features there is a separate transformation.