  Modern Convolutional Neural Networks (CNN) are extremely powerful on a range
of computer vision tasks. However, their performance may degrade when the data
is characterised by large intra-class variability caused by spatial
transformations. The Spatial Transformer Network (STN) is currently the method
of choice for providing CNNs the ability to remove those transformations and
improve performance in an end-to-end learning framework. In this paper, we
propose Densely Fused Spatial Transformer Network (DeSTNet), which, to our best
knowledge, is the first dense fusion pattern for combining multiple STNs.
Specifically, we show how changing the connectivity pattern of multiple STNs
from sequential to dense leads to more powerful alignment modules. Extensive
experiments on three benchmarks namely, MNIST, GTSRB, and IDocDB show that the
proposed technique outperforms related state-of-the-art methods (i.e., STNs and
CSTNs) both in terms of accuracy and robustness.
