DeSTNet: Densely Fused Spatial Transformer Networks

Understand

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.