  Many machine vision applications, such as semantic segmentation and depth
prediction, require predictions for every pixel of the input image. Models for
such problems usually consist of encoders which decrease spatial resolution
while learning a high-dimensional representation, followed by decoders who
recover the original input resolution and result in low-dimensional
predictions. While encoders have been studied rigorously, relatively few
studies address the decoder side. This paper presents an extensive comparison
of a variety of decoders for a variety of pixel-wise tasks ranging from
classification, regression to synthesis. Our contributions are: (1) Decoders
matter: we observe significant variance in results between different types of
decoders on various problems. (2) We introduce new residual-like connections
for decoders. (3) We introduce a novel decoder: bilinear additive upsampling.
(4) We explore prediction artifacts.
