Due to limited resources and environmental pollution, monitoring the geological
environment has become essential for many countries' sustainable development. As various …

Background modeling for foreground detection is often used in different applications to
model the background and then detect the moving objects in the scene like in video …

Abstract Self-Supervised Learning (SSL) surmises that inputs and pairwise positive
relationships are enough to learn meaningful representations. Although SSL has recently …

Novelty detection is the process of identifying the observation (s) that differ in some respect
from the training observations (the target class). In reality, the novelty class is often absent …

With the recent advances in deep neural networks, anomaly detection in multimedia has
received much attention in the computer vision community. While reconstruction-based …

We propose a one-class neural network (OC-NN) model to detect anomalies in complex
data sets. OC-NN combines the ability of deep networks to extract a progressively rich …

A popular method for anomaly detection is to use the generator of an adversarial network to
formulate anomaly score over reconstruction loss of input. Due to the rare occurrence of …

Novelty detection is the problem of identifying whether a new data point is considered to be
an inlier or an outlier. We assume that training data is available to describe only the inlier …

Principal component analysis (PCA) is one of the most widely used dimension reduction
techniques. A related easier problem is termed subspace learning or subspace estimation …

In this paper, we address the subspace clustering problem. Given a set of data samples
(vectors) approximately drawn from a union of multiple subspaces, our goal is to cluster the …