Abstract
We present a Deep Convolutional Neural Network architecture which serves as a generic image-to-image regressor
that can be trained end-to-end without any further machinery. Our proposed architecture, the Recursively Branched
Deconvolutional Network (RBDN), develops a cheap multicontext image representation very early on using an efficient
recursive branching scheme with extensive parameter sharing and learnable upsampling. This multi-context representation is subjected to a highly non-linear locality preserving transformation by the remainder of our network comprising of a series of convolutions/deconvolutions without
any spatial downsampling. The RBDN architecture is fully
convolutional and can handle variable sized images during
inference. We provide qualitative/quantitative results on 3
diverse tasks: relighting, denoising and colorization and
show that our proposed RBDN architecture obtains comparable results to the state-of-the-art on each of these tasks
when used off-the-shelf without any post processing or taskspecific architectural modifications.