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International Journal of Computer Vision
Erschienen in: International Journal of Computer Vision 11-12/2019

14.03.2019

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

verfasst von: Zbigniew Wojna, Vittorio Ferrari, Sergio Guadarrama, Nathan Silberman, Liang-Chieh Chen, Alireza Fathi, Jasper Uijlings

Erschienen in: International Journal of Computer Vision | Ausgabe 11-12/2019

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Abstract

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.
Vorheriger Artikel A Differential Approach to Shape from Polarisation: A Level-Set Characterisation
Nächster Artikel Reflectance and Shape Estimation with a Light Field Camera Under Natural Illumination

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Metadaten
Titel
The Devil is in the Decoder: Classification, Regression and GANs
verfasst von
Zbigniew Wojna
Vittorio Ferrari
Sergio Guadarrama
Nathan Silberman
Liang-Chieh Chen
Alireza Fathi
Jasper Uijlings
Publikationsdatum
14.03.2019
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 11-12/2019
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-019-01170-8

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