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

The Devil is in the Decoder: Classification, Regression and GANs