Kernel Density Network for Quantifying Regression Uncertainty in Face Alignment

@inproceedings{Chen2018KernelDN,
  title={Kernel Density Network for Quantifying Regression Uncertainty in Face Alignment},
  author={Lisha Chen},
  year={2018}
}
  • Lisha Chen
  • Published 2018
For deep neural networks, it is important to quantify the uncertainty in its predictions. So a probabilistic neural network with a Gaussian assumption was widely used. However, in real data especially image data, the Gaussian assumption typically cannot hold. We are interested in modeling a more general distribution, e.g. multi-modal or asymmetric distribution. Therefore, a kernel density neural network is proposed. We adopt state-of-the-art neural network architecture and propose a new loss… CONTINUE READING

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