Generalized Deep Image to Image Regression

• Published in: arXiv.org
• Main Authors: Santhanam, Venkataraman, Morariu, Vlad I, Davis, Larry S
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 10.12.2016
• Subjects: Artificial neural networks; Neural networks; Noise reduction; Representations

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 multi-context 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 task-specific architectural modifications.