Salience-affected neural networks

In this research, the salience of an entity refers to its state or quality of standing out, or receiving increased attention, relative to neighboring entities. By neighbouring entities we refer to both spatial (i.e. similar visual objects) and temporal (i.e. related concepts). In this research we model the effect of non-local connections using an ANN, creating a salience-affected neural network (SANN). We adapt an ANN to embody the capacity to respond to an input salience signal and to produce a reverse salience signal during testing. The input salience signal applied during training to each node has the effect of varying the node’s thresholds, depending on the activation level of the node. Each node produces a nodal reverse salience signal during testing (a measure of the threshold bias for the individual node). The reverse salience signal is defined as the summation of the nodal reverse salience signals observed at each node.

Grantor dc:publisher.institution

Department of Electrical Engineering

Year dc:date.issued

2009

Author dc:creator

• Remmelzwaal, Leendert Amani

Advisors dc:contributor.advisor

• Tapson, Jonathan
• Ellis, GFR

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/12111

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/12111