6,774 research outputs found
Information theoretic novelty detection
We present a novel approach to online change detection problems when the training sample size is small. The proposed approach is based on estimating the expected information content of a new data point and allows an accurate control of the false positive rate even for small data sets. In the case of the Gaussian distribution, our approach is analytically tractable and closely related
to classical statistical tests. We then propose an approximation scheme to extend our approach to the case of the mixture of Gaussians. We evaluate extensively our approach on synthetic data and on three real benchmark data
sets. The experimental validation shows that our method maintains a good overall accuracy, but significantly improves the control over the false positive rate
The Nature of Novelty Detection
Sentence level novelty detection aims at reducing redundant sentences from a
sentence list. In the task, sentences appearing later in the list with no new
meanings are eliminated. Aiming at a better accuracy for detecting redundancy,
this paper reveals the nature of the novelty detection task currently
overlooked by the Novelty community Novelty as a combination of the partial
overlap (PO, two sentences sharing common facts) and complete overlap (CO, the
first sentence covers all the facts of the second sentence) relations. By
formalizing novelty detection as a combination of the two relations between
sentences, new viewpoints toward techniques dealing with Novelty are proposed.
Among the methods discussed, the similarity, overlap, pool and language
modeling approaches are commonly used. Furthermore, a novel approach, selected
pool method is provided, which is immediate following the nature of the task.
Experimental results obtained on all the three currently available novelty
datasets showed that selected pool is significantly better or no worse than the
current methods. Knowledge about the nature of the task also affects the
evaluation methodologies. We propose new evaluation measures for Novelty
according to the nature of the task, as well as possible directions for future
study.Comment: This paper pointed out the future direction for novelty detection
research. 37 pages, double spaced versio
Novelty Detection for Robot Neotaxis
The ability of a robot to detect and respond to changes in its environment is
potentially very useful, as it draws attention to new and potentially important
features. We describe an algorithm for learning to filter out previously
experienced stimuli to allow further concentration on novel features. The
algorithm uses a model of habituation, a biological process which causes a
decrement in response with repeated presentation. Experiments with a mobile
robot are presented in which the robot detects the most novel stimulus and
turns towards it (`neotaxis').Comment: 7 pages, 5 figures. In Proceedings of the Second International
Conference on Neural Computation, 200
Continual Novelty Detection
Novelty Detection methods identify samples that are not representative of a
model's training set thereby flagging misleading predictions and bringing a
greater flexibility and transparency at deployment time. However, research in
this area has only considered Novelty Detection in the offline setting.
Recently, there has been a growing realization in the computer vision community
that applications demand a more flexible framework - Continual Learning - where
new batches of data representing new domains, new classes or new tasks become
available at different points in time. In this setting, Novelty Detection
becomes more important, interesting and challenging. This work identifies the
crucial link between the two problems and investigates the Novelty Detection
problem under the Continual Learning setting. We formulate the Continual
Novelty Detection problem and present a benchmark, where we compare several
Novelty Detection methods under different Continual Learning settings.
We show that Continual Learning affects the behaviour of novelty detection
algorithms, while novelty detection can pinpoint insights in the behaviour of a
continual learner. We further propose baselines and discuss possible research
directions. We believe that the coupling of the two problems is a promising
direction to bring vision models into practice
Novelty detection in video surveillance using hierarchical neural networks
Abstract. A hierarchical self-organising neural network is described for the detection of unusual pedestrian behaviour in video-based surveillance systems. The system is trained on a normal data set, with no prior information about the
scene under surveillance, thereby requiring minimal user input. Nodes use a trace activation rule and feedforward connections, modified so that higher layer nodes are sensitive to trajectory segments traced across the previous layer. Top layer nodes have binary lateral connections and corresponding “novelty accumulator” nodes. Lateral connections are set between co-occurring nodes, generating a signal to prevent accumulation of the novelty measure along normal sequences. In abnormal sequences the novelty accumulator nodes are allowed to increase their activity, generating an alarm state
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