Neural Networks for Modeling Esthetic Selection
Some real world problems require significant human interaction for labeling the data, which is very expensive. Worse, in some cases, the exercise of human judgement is inherently subjective and contextual, and so the entire labeling must be done in one session, which may be too long. Our domain is the automatic generation of Mondrian-like images with an interactive interface for the user to select images. We use back-propagation neural networks to learn an approximation of a viewer's aesthetic...[Show more]
|Collections||ANU Research Publications|
|Source:||Neural Information Processing - Proceedings of the fourteenth International Conference on Neural Information Processing (ICONIP 2007)|
|01_Gedeon_Neural_Networks_for_Modeling_2008.pdf||741.83 kB||Adobe PDF||Request a copy|
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