Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Why Out-of-distribution Detection in CNNs Does Not Like Mahalanobis --
  and What to Use Instead   Convolutional neural networks applied for real-world classification tasks
need to recognize inputs that are far or out-of-distribution (OoD) with respect
to the known or training data. To achieve this, many methods estimate
class-conditional posterior probabilities and use confidence scores obtained
from the posterior distributions. Recent works propose to use multivariate
Gaussian distributions as models of posterior distributions at different layers
of the CNN (i.e., for low- and upper-level features), which leads to the
confidence scores based on the Mahalanobis distance. However, this procedure
involves estimating probability density in high dimensional data using the
insufficient number of observations (e.g. the dimensionality of features at the
last two layers in the ResNet-101 model are 2048 and 1024, with ca. 1000
observations per class used to estimate density). In this work, we want to
address this problem. We show that in many OoD studies in high-dimensional
data, LOF-based (Local Outlierness-Factor) methods outperform the parametric,
Mahalanobis distance-based methods. This motivates us to propose the
nonparametric, LOF-based method of generating the confidence scores for CNNs.
We performed several feasibility studies involving ResNet-101 and
EffcientNet-B3, based on CIFAR-10 and ImageNet (as known data), and CIFAR-100,
SVHN, ImageNet2010, Places365, or ImageNet-O (as outliers). We demonstrated
that nonparametric LOF-based confidence estimation can improve current
Mahalanobis-based SOTA or obtain similar performance in a simpler way.

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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: eess
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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: econ
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The categories of this paper are: hep-ex
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The categories of this paper are: supr-con
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat