3 research outputs found
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
Improving auto-encoder novelty detection using channel attention and entropy minimization
Novelty detection is a important research area which mainly solves the
classification problem of inliers which usually consists of normal samples and
outliers composed of abnormal samples. We focus on the role of auto-encoder in
novelty detection and further improved the performance of such methods based on
auto-encoder through two main contributions. Firstly, we introduce attention
mechanism into novelty detection. Under the action of attention mechanism,
auto-encoder can pay more attention to the representation of inlier samples
through adversarial training. Secondly, we try to constrain the expression of
the latent space by information entropy. Experimental results on three public
datasets show that the proposed method has potential performance for novelty
detection
One-Class Classification: A Survey
One-Class Classification (OCC) is a special case of multi-class
classification, where data observed during training is from a single positive
class. The goal of OCC is to learn a representation and/or a classifier that
enables recognition of positively labeled queries during inference. This topic
has received considerable amount of interest in the computer vision, machine
learning and biometrics communities in recent years. In this article, we
provide a survey of classical statistical and recent deep learning-based OCC
methods for visual recognition. We discuss the merits and drawbacks of existing
OCC approaches and identify promising avenues for research in this field. In
addition, we present a discussion of commonly used datasets and evaluation
metrics for OCC