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Strategies for Conceptual Change in Convolutional Neural Networks

Maarten Grachten, Carlos Eduardo Cancino Chacón

A remarkable feature of human beings is their capacity for creative behavior, refering to their ability to react to problems in ways that are novel, surprising, and useful. Transformational creativity is a form of creativity where the creative behavior is induced by a transformation of the actor's conceptual space, that is, the representational system with which the actor interprets its environment. In this report, we focus on ways of adapting systems of learned representations as they switch to performing one task to performing another. We describe an experimental comparison of multiple strategies for adaptation of learned features, and evaluate how effectively each of these strategies realizes the adaptation, in terms of the amount of training, and in terms of their ability to cope with restricted availability of training data. We show, among other things, that across handwritten digits, natural images, and classical music, adaptive strategies are systematically more effective than a baseline method that starts learning from scratch.

Citation: Grachten M., Cancino Chacón C.: Strategies for Conceptual Change in Convolutional Neural Networks. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2015-04,