Nanni, L.; Lumini, A.; Fantozzi, C. Exploring the Potential of Ensembles of Deep Learning Networks for Image Segmentation. Information 2023, 14, 657, doi:10.3390/info14120657.
Nanni, L.; Lumini, A.; Fantozzi, C. Exploring the Potential of Ensembles of Deep Learning Networks for Image Segmentation. Information 2023, 14, 657, doi:10.3390/info14120657.
Nanni, L.; Lumini, A.; Fantozzi, C. Exploring the Potential of Ensembles of Deep Learning Networks for Image Segmentation. Information 2023, 14, 657, doi:10.3390/info14120657.
Nanni, L.; Lumini, A.; Fantozzi, C. Exploring the Potential of Ensembles of Deep Learning Networks for Image Segmentation. Information 2023, 14, 657, doi:10.3390/info14120657.
Abstract
To identify objects in images, a complex set of skills is needed that includes understanding the context and being able to determine the borders of objects. In computer vision, this task is known as semantic segmentation and it involves categorizing each pixel in an image. It is crucial in many real-world situations: for autonomous vehicles, it enables the identification of objects in the surrounding area; in medical diagnosis, it enhances the ability to detect dangerous pathologies early, thereby reducing the risk of serious consequences. In this study, we compare the performance of various ensembles of convolutional and transformer neural networks. Ensembles can be created, e.g, by varying the loss function, data augmentation method or the learning rate strategy. Our proposed ensemble, which is based on the simple average rule, demonstrates exceptional performance on several datasets. All the resources used in this study are available online at the following GitHub repository: https://github.com/LorisNanni.
Keywords
deep learning; ensembles; segmentation; transformers
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
