Corpus ID: 203593757

On Incorporating Semantic Prior Knowlegde in Deep Learning Through Embedding-Space Constraints

@article{Teney2019OnIS,
  title={On Incorporating Semantic Prior Knowlegde in Deep Learning Through Embedding-Space Constraints},
  author={Damien Teney and Ehsan Abbasnejad and Anton van den Hengel},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.13471}
}
The knowledge that humans hold about a problem often extends far beyond a set of training data and output labels. [...] Key Method Existing methods to use these annotations, including auxiliary losses and data augmentation, cannot guarantee the strict inclusion of these relations into the model since they require a careful balancing against the end-to-end objective. Our method uses these relations to shape the embedding space of the model, and treats them as strict constraints on its learned representations.Expand
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