Title: Dense Transformer Networks. Abstract: The key idea of current deep learning methods for dense prediction
is to apply a model on a regular patch centered on each pixel to
make pixel-wise predictions. These methods are limited in the sense
that the patches are determined by network architecture instead of
learned from data. In this work, we propose the dense transformer
networks, which can learn the shapes and sizes of patches from data.
The dense transformer networks employ an encoder-decoder
architecture, and a pair of dense transformer modules are inserted
into each of the encoder and decoder paths. The novelty of this work
is that we provide technical solutions for learning the shapes and
sizes of patches from data and efficiently restoring the spatial
correspondence required for dense prediction. The proposed dense
transformer modules are differentiable, thus the entire network can
be trained. We apply the proposed networks on natural and biological
image segmentation tasks and show superior performance is achieved
in comparison to baseline methods.