Exploring Underlying Features in Hidden Layers of Neural Network.

The black box nature of artificial neural networks limits the understanding of internal mechanisms and processes that happen inside hidden layers. The introduction of deep neural networks and efficient layer-wise training methods has enabled researchers to study how features are learnt through different layers of neural networks. However, there has been limited research on mapping input features to neural network weights in order to understand how features are represented in the layers. This research proposes a novel component model to establish the relationship between input features and neural network weights. This will aid in optimizing transfer learning models by only extracting relevant weights instead of all the weights in the layers. The proposed model is evaluated using standard IRIS and a set of modified IRIS datasets. Classification experiments are conducted, and the results are evaluated to verify the quality of the dataset. A visualization of input features and components through the proposed model is presented using t-SNE to indicate the impact of changes in the input features. From the results, it is concluded that the proposed component model provides core knowledge in the form of weights representing the input features that are learnt through training. The proposed work will aid in designing component-based transfer learning, which would improve the speed. Also, the components could be used as pretrained testing models for similar work with large datasets.