Abstract
Convolutional Neural Networks (ConvNets or CNNs) are that class of Neural Networks that have confirm very effective in areas such as classification and image recognition. Graph convolution neural network is that area of work that deals with the generalization of well-established neural models like convnets on structured datasets as there are numerous important problems that can be framed as learning from graph data. This paper focused to overcome one of the major weaknesses GCNN poses i.e. GCNN are Translational Invariant means these neural models are unable to identify the position of one object with respect to another. For example, the model will predict a car if it sees a bunch of random car parts like wheels, steering, headlights, and so on because all the key features are there. So, the main problem is to identify that the car parts are not in the correct position relative to another. We performed the experiments on two benchmark datasets, MNIST and smallNORB, and the accuracies obtained with our proposed models are comparably high. Thus the technique is able to preserve the spatial relationship among objects in the given structured dataset is the main motivation behind this paper.
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Yadav, R.K., Abhishek, Shukla, P., Tabassum, N., Tomar, R.S., Verma, S. (2021). Spatial Information Preservation in Graph Convolutional Neural Network. In: Tomar, R.S., et al. Communication, Networks and Computing. CNC 2020. Communications in Computer and Information Science, vol 1502. Springer, Singapore. https://doi.org/10.1007/978-981-16-8896-6_17
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DOI: https://doi.org/10.1007/978-981-16-8896-6_17
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