Operational Neural Networks (ONNs)
Operational Neural Networks (ONNs)
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What is an Operational Neural Network?
What is an Operational Neural Network?
Convolutional Neural Networks (CNNS) by sharing a linear neuron model (as in Multilayer Perceptrons) with two additional constraints (local connections and weight sharing), restrict the learning capability of each individual neuron. It is, therefore, not surprising that in many challenging problems only the deep CNNs with a massive complexity and depth can achieve the required diversity and the learning performance. To address this drawback and also to accomplish a more generalized model (similar to biological neurons) where nonlinearity can exist in different forms, we introduced the Generalized Operational Perceptrons (GOPs) that can encapsulate linear and nonlinear operators. By exploiting similar properties to GOPs, Operational Neural Networks are heterogeneous neural networks with enhanced learning capabilities.
Image denoising
Image denoising
Image transformation
Image transformation
Image Synthesis
Image Synthesis
Self-organized Operational Neural Network?
Self-organized Operational Neural Network?
In order to address the training time complexity issues of standard ONNs and achieve an ultimate heterogeneity level to boost the network diversity along with computational efficiency, we propose Self-organized ONNs (Self-ONNs) with generative neurons that have the ability to adapt (optimize) the nodal operator of each connection during the training process. Therefore, Self-ONNs can have an utmost heterogeneity level required by the learning problem at hand. Moreover, this ability voids the need of having a fixed operator set library and the prior operator search within the library in order to find the best possible set of operators. We further formulate the training method to back-propagate the error through the operational layers of Self-ONNs.
References
References
The list provided in the following may be incomplete. The complete list of papers related to this topic can be found in the lists of journal papers and conference papers.
S. Kiranyaz, J. Malik, H.B. Abdallah, T. Ince, A. Iosifidis and M. Gabbouj, “Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity”, Neural Computing and Applications, accepted November 2020
S. Kiranyaz, J. Malik, H.B. Abdallah, T. Ince, A. Iosifidis and M. Gabbouj, "Self-Organized Operational Neural Networks with Generative Neurons", arXiv:2004.11778, 2020
S. Kiranyaz, T. Ince, A. Iosifidis and M. Gabbouj, “Progressive Operational Perceptrons”, Neurocomputing, vol. 224, pp. 142-154, 2017
S. Kiranyaz, T. Ince, A. Iosifidis and M. Gabbouj, “Generalized Model of Biological Neural Networks: Progressive Operational Perceptrons”, INNS International Joint Conference on Neural Networks, Anchorage, Alaska, USA, 2017
S. Kiranyaz, T. Ince, A. Iosifidis and M. Gabbouj, “Operational Neural Networks”, Neural Computing and Applications, vol. 32, pp. 6645–6668, 2020
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