On-line Novelty Detection With Application to Mobile Robotics Stephen Marsland Imaging Science and Biomedical Engineering University of Manchester

On-line Novelty Detection

With Application to Mobile Robotics

Stephen Marsland

Imaging Science and Biomedical Engineering

University of Manchester

Novelty Detection

Highlighting inputs that differ in some way from the ‘normal’ stream of inputs

Novelty Detection in Animals

• Detect unusual stimuli– reduce the amount of

information that needs to be processed

– enables the animal to focus on important information

– helps avoid predators

Novelty Detection in Artificial Learning Systems

• Detect unusual stimuli– reduce the amount of information that needs

to be processed– enables the learner to focus on important

information– helps avoid predators!

…


• Detect unusual stimuli– Reduce the amount the system has to learn

• Detect anomalies in a datastream

• Detect when class pdfs or mixture ratios change

• Detect when a new class appears

Statistical Outlier Detection





• Applications include:

– Machine fault monitoring

– Medical diagnosis

– Inspection agent

– Pre-processing for inputs

Approach

• Learn a representation of normality

• Evaluate how well each input fits into the acquired model

• Highlight those inputs that are not well represented by the model

p(x|C1)P(C1)

p(x|C2)P(C2)

Requirements

• Learning algorithm

• Method of evaluating the novelty of a new input

• Some parameter tuning (how much should the learning algorithm generalise)

Possible Approaches:Auto-assocative network

Possible Approaches: Auto-assocative network

• Learn to reproduce inputs at the outputs, so that the bottleneck learns a lower dimensional representation - principal components

• After training, will settle to a trained input, and subtracting the input from the output shows the novelty in the current input

Possible Approaches: Mixtures of Gaussians

• Train a Gaussian Mixture Model to represent the training data (simple using EM)

• Look for input points that are not contained within any of the mixtures

How Do Animals Detect Novelty?

• Habituation• Reduction in synaptic efficacy when a

stimulus is seen repeatedly without ill effect

Habituation

• Simple to model

• Gives a method of deciding how familiar an input is – how frequently has it been seen before

• This is what is needed for novelty detection

Habituation

Novelty Detection Using Habituation

• Some form of clustering algorithm• Learn a model of inputs• Habituate those nodes that fire frequently

and therefore recognise familiar features

• If an input causes an unhabituated node to fire it is novel

• If an input is not represented by the network it is novel

Focus: Inspection

• Train a robot to recognise all of the normal features of some environment, as perceived by the robot’s sensors

• Any features that are not recognised by the system (novelties) are possible faults

Requirements for Novelty Detection Inspection

• On-line learning

• Robustness to some ‘novel’ inputs

• Quantification of the amount of novelty in each input

• Environment specificity

The ‘Grow When Required’ Network

• A self-organising neural network that can operate on-line

• Produces perfectly-topology preserving maps

• Has novelty detection capability built in through habituating synapses

• Is similar to, but derived independently from, FOSART – Baraldi & Alpaydin





Topology Preservation


5,000 inputs 15,000 inputs


25,000 inputs 35,000 inputs

Using the GWR as a Novelty Filter



Training a Filter

• Start with an empty GWR network

• As the robot travels down the corridor, sensory perceptions as taken as inputs to the network

• The network adapts to learn about each new input and assesses them for novelty

• It may take a few runs to learn about an environment

Inspection Using Novelty Detection

16 sonar sensors

16 IR sensors

Bumpers

Monochrome camera


Environment A





Environment B Environment C



Forgetting

Why Not Just Use the Self-Organising Map?

Small SOM

Large SOM

GWR

Using Visual Input

• Sonar inputs are fairly low dimensional, and do not provide that much information about the environment

• Can also consider using a camera

• The camera mounted on the robot is a low resolution monochrome camera

The Visual Magnet

• Ensure that the robot faces in the same direction each time that it is one position

• Perform edge detection, generate a histogram and rotate the turret of the robot to centre the largest element of the histogram

• Works approximately 75% of the time

Generating an Input Vector

• Plenty of choice:– Edge detection histograms– Principal component filters– Raw image– ‘Fingerprint’ – subset of image pixels chosen

in some way

• All are essentially hacks

Visual Inspection

Principal Component Filters Spiral ‘Fingerprint’

Visual Inspection

Spiral ‘Fingerprint’

“…the novelty of the wife in the best friend’s bed lies neither in the wife, the friend, nor the bed, but in the conjunction of the three.”

O’Keefe and Nadel, 1978

Requirements for Novelty Detection Inspection

Selecting Different Novelty Filters

• A bank of novelty filters are used, with each one trained in a different environment

• A set of familiarity vectors keep a record of how well each novelty filter recognises the current perceptions

Selecting Different Novelty Filters

• For each input:– For each filter:

• Compute the output of each novelty filter• Reduce the element of the familiarity vector

for the filter proportional to the output• Increase the elements of the familiarity

vector for every other filter so that the sum remains normalised

Selecting a Filter

Adding New Filters

• What if none of the filters represents an environment well?– Total novelty is very high– Several of the filters have similar familiarity

scores

• Can then automatically add a new filter and train it in that environment

Adding New Filters

Other Applications

• Ball-bearings• Medical• Landmark selection

Conclusions• Novelty detection is a useful capability for

learning systems• The GWR network enables on-line novelty

detection and hence, robot inspection• Several filters trained in different

environments provides environment-specific novelty detection, with the system deciding which environment it is currently in

• The system can train new filters as required

On-line Novelty Detection

With Application to Mobile Robotics

Stephen Marsland Imaging Science and Biomedical Engineering

University of [email protected]

http://www.isbe.man.ac.uk/~srm/

mailto:[email protected]

Documents

On-line Novelty Detection With Application to Mobile Robotics Stephen Marsland Imaging Science and Biomedical Engineering University of Manchester