Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-supervised novelty detection using ensembles with regularized
  disagreement   Deep neural networks often predict samples with high confidence even when
they come from unseen classes and should instead be flagged for expert
evaluation. Current novelty detection algorithms cannot reliably identify such
near OOD points unless they have access to labeled data that is similar to
these novel samples. In this paper, we develop a new ensemble-based procedure
for semi-supervised novelty detection (SSND) that successfully leverages a
mixture of unlabeled ID and novel-class samples to achieve good detection
performance. In particular, we show how to achieve disagreement only on OOD
data using early stopping regularization. While we prove this fact for a simple
data distribution, our extensive experiments suggest that it holds true for
more complex scenarios: our approach significantly outperforms state-of-the-art
SSND methods on standard image data sets (SVHN/CIFAR-10/CIFAR-100) and medical
image data sets with only a negligible increase in computation cost.

36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas