• Corpus ID: 239998219

A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition

@article{Tian2021AGP,
  title={A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition},
  author={Junjiao Tian and Dylan Yung and Yen-Chang Hsu and Zsolt Kira},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.14577}
}
It is well known that vision classification models suffer from poor calibration in the face of data distribution shifts. In this paper, we take a geometric approach to this problem. We propose Geometric Sensitivity Decomposition (GSD) which decomposes the norm of a sample feature embedding and the angular similarity to a target classifier into an instance-dependent and an instance-independent component. The instance-dependent component captures the sensitive information about changes in the… 
Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data
TLDR
This work proposes to characterize the spectrum of OOD data using two types of distribution shifts: covariate shift and concept shift, and proposes a geometricallyinspired method (Geometric ODIN) to improve OOD detection under both shifts with only in-distribution data.

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