Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: One-Class Convolutional Neural Network   We present a novel Convolutional Neural Network (CNN) based approach for one
class classification. The idea is to use a zero centered Gaussian noise in the
latent space as the pseudo-negative class and train the network using the
cross-entropy loss to learn a good representation as well as the decision
boundary for the given class. A key feature of the proposed approach is that
any pre-trained CNN can be used as the base network for one class
classification. The proposed One Class CNN (OC-CNN) is evaluated on the
UMDAA-02 Face, Abnormality-1001, FounderType-200 datasets. These datasets are
related to a variety of one class application problems such as user
authentication, abnormality detection and novelty detection. Extensive
experiments demonstrate that the proposed method achieves significant
improvements over the recent state-of-the-art methods. The source code is
available at : github.com/otkupjnoz/oc-cnn.

14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex