Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training-Free Uncertainty Estimation for Dense Regression: Sensitivity
  as a Surrogate   Uncertainty estimation is an essential step in the evaluation of the
robustness for deep learning models in computer vision, especially when applied
in risk-sensitive areas. However, most state-of-the-art deep learning models
either fail to obtain uncertainty estimation or need significant modification
(e.g., formulating a proper Bayesian treatment) to obtain it. Most previous
methods are not able to take an arbitrary model off the shelf and generate
uncertainty estimation without retraining or redesigning it. To address this
gap, we perform a systematic exploration into training-free uncertainty
estimation for dense regression, an unrecognized yet important problem, and
provide a theoretical construction justifying such estimations. We propose
three simple and scalable methods to analyze the variance of outputs from a
trained network under tolerable perturbations: infer-transformation,
infer-noise, and infer-dropout. They operate solely during the inference,
without the need to re-train, re-design, or fine-tune the models, as typically
required by state-of-the-art uncertainty estimation methods. Surprisingly, even
without involving such perturbations in training, our methods produce
comparable or even better uncertainty estimation when compared to
training-required state-of-the-art methods.

28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph