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Erschienen in:
2018 | OriginalPaper | Buchkapitel
New Architecture of Correlated Weights Neural Network for Global Image Transformations
verfasst von : Sławomir Golak, Anna Jama, Marcin Blachnik, Tadeusz Wieczorek
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2018
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Abstract
The paper describes a new extension of the convolutional neural network concept. The developed network, similarly to the CNN, instead of using independent weights for each neuron in the network uses related weights. This results in a small number of parameters optimized in the learning process, and high resistance to overtraining. However unlike the CNN, instead of sharing weights, the network takes advantage of weights correlated with coordinates of a neuron and its inputs, calculated by a dedicated subnet. This solution allows the neural layer of the network to perform global transformation of patterns what was unachievable for convolutional layers. The new network concept has been confirmed by verification of its ability to perform typical image affine transformations such as translation, scaling and rotation.