Corpus ID: 236912480

Calibrated Adversarial Refinement for Stochastic Semantic Segmentation

@inproceedings{Kassapis2020CalibratedAR,
  title={Calibrated Adversarial Refinement for Stochastic Semantic Segmentation},
  author={Elias Kassapis and G. Dikov and Deepak K. Gupta and C. Nugteren},
  year={2020}
}
In semantic segmentation tasks, input images can often have more than one plausible interpretation, thus allowing for multiple valid labels. To capture such ambiguities, recent work has explored the use of probabilistic networks that can learn a distribution over predictions. However, these do not necessarily represent the empirical distribution accurately. In this work, we present a strategy for learning a calibrated predictive distribution over semantic maps, where the probability associated… Expand

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