Semi-Supervised Learning with Ensemble Deep Learning Networks for Descriptor Generation

Abstract

Deep neural networks and machine learning have made many real-world tasks easier. They can analyze large data sets that humans can't. Deep neural networks can be utilized for image, video, and sound matching, making them an intriguing research area. This research focuses on semi-supervised training and proposes a "enhanced semi-supervised" learning strategy. Robots can learn from labeled and unlabeled data with semi-supervised learning. Supervised learning employs labeled data. Semi-supervised learning is used to govern the Deep neural network's output. Assigning values to input groups and accessing the output area have no interaction. This method seeks to deliver a more efficient learning approach with an equitable distribution of output throughout the output field of space, and the authors developed an ensemble strategy based on three deep learning approaches (Convolution Deep neural network, Alexnet, and MobileNetv2). The result is a more effective learning approach. The handwritten digit experiment was 90.74 percent accurate, whereas Alzheimer's detection was 99.76 percent accurate. When the proposed strategy was used on two experimental data sets, the accuracy was better than when different applications used Siamese Deep neural networks.