Abstract
We propose the Temporal Correlation Net (TCN) as an object recognition system implementing three basic principles: forming temporal groups of features, learning in a hierarchical structure, and using feedback to predict future input. It is a further development of the Temporal Correlation Graph [1] and shows improved performance on standard datasets like ETH80, COIL100, and ALOI. In contrast to its predecessor it can be trained online on all levels rather than in a level per level batch mode. Training images are presented in temporal order showing objects undergoing specific transformations under viewing conditions the system is supposed to learn invariance under. Computation time and memory demands are low because of sparse learned connectivity and efficient handling of neural activities.
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Leßmann, M., Würtz, R.P. (2014). Online Learning of Invariant Object Recognition in a Hierarchical Neural Network. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_54
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DOI: https://doi.org/10.1007/978-3-319-11179-7_54
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