/uacnn

Uncertainty-Aware CNN - Uncertainty propagation in CNN

Primary LanguagePythonMIT LicenseMIT

UA-CNN: Uncertainty-aware CNN

Propagation of Gaussian uncertainty through typical CNN building blocks.

Technical report can be found here. If you find UA-CNN useful in your research, please consider adding the following citation:

@misc{uacnn,
      author = {Christos, Tzelepis and Ioannis, Patras},
      title = {{UA-CNN}: Uncertainty Propagation in Convolutional NeuralNetworks},
      year = {2020},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/chi0tzp/uacnn}},
}

  • UAConv2d: Uncertainty-aware 2D convolution

  • UAAvgPool2d: Uncertainty-aware 2D pooling

  • UALinear: Uncertainty-aware linear (fully-connected) layer

  • UAReLU: Uncertainty-aware rectified linear unit for various amounts of input uncertainty

  • Expected BCE loss (UABCELoss) for various amounts of input uncertainty (dashed red lines) compared to standard BCE loss