Deep Structured Prediction with Nonlinear Output Transformations

By Colin Graber, Ofer Meshi, Alexander Gerhard Schwing. 2018

27 citations recorded by the source corpus.

Deep structured models are widely used for tasks like semantic segmentation, where explicit correlations between variables provide important prior information which generally helps to reduce the data needs of deep nets. However, current deep structured models are restricted by oftentimes very local neighborhood structure, which cannot be increased for computational complexity reasons, and by the fact that the output configuration, or a representation thereof, cannot be transformed further. Very recent approaches which address those issues include graphical model inference inside deep nets so as to permit subsequent non-linear output space transformations. However, optimization of those formulations is challenging and not well understood. Here, we develop a novel model which generalizes existing approaches, such as structured prediction energy networks, and discuss a formulation which maintains applicability of existing inference techniques.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Structured Convolutional Kernel Networks for Airline Crew Scheduling (2021, methodology): “, 2020) and NLStruct (Graber et al., 2018) provide better results (4.”