Robust outlier detection by de-biasing VAE likelihoods

@article{Chauhan2022RobustOD,
  title={Robust outlier detection by de-biasing VAE likelihoods},
  author={Kushal Chauhan and Pradeep Shenoy and Manish Gupta and D. Sridharan},
  journal={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022},
  pages={9871-9880}
}
Deep networks often make confident, yet, incorrect, predictions when tested with outlier data that is far removed from their training distributions. Likelihoods computed by deep generative models (DGMs) are a candidate metric for outlier detection with unlabeled data. Yet, previous studies have shown that DGM likelihoods are unreliable and can be easily biased by simple transformations to input data. Here, we examine outlier detection with variational autoencoders (VAEs), among the simplest of… 

Shaken, and Stirred: Long-Range Dependencies Enable Robust Outlier Detection with PixelCNN++

TLDR
It is shown that biases in PixelCNN++ likelihoods arise primarily from predictions based on local dependencies, and two families of bijective transformations are proposed that are computationally inexpensive and readily applied at evaluation time to achieve robust outlier detection on images with deep generative models.

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