Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Geometric Perspective towards Neural Calibration via Sensitivity
  Decomposition   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 input while the instance-independent
component represents the insensitive information serving solely to minimize the
loss on the training dataset. Inspired by the decomposition, we analytically
derive a simple extension to current softmax-linear models, which learns to
disentangle the two components during training. On several common vision
models, the disentangled model outperforms other calibration methods on
standard calibration metrics in the face of out-of-distribution (OOD) data and
corruption with significantly less complexity. Specifically, we surpass the
current state of the art by 30.8% relative improvement on corrupted CIFAR100 in
Expected Calibration Error. Code available at
https://github.com/GT-RIPL/Geometric-Sensitivity-Decomposition.git.

26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg