Article
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Deep ensembles based on Stochastic Activations for Semantic Segmentation
Version 1
: Received: 28 July 2021 / Approved: 30 July 2021 / Online: 30 July 2021 (09:36:28 CEST)
A peer-reviewed article of this Preprint also exists.
Lumini, A.; Nanni, L.; Maguolo, G. Deep Ensembles Based on Stochastic Activations for Semantic Segmentation. Signals 2021, 2, 820-833. Lumini, A.; Nanni, L.; Maguolo, G. Deep Ensembles Based on Stochastic Activations for Semantic Segmentation. Signals 2021, 2, 820-833.
Abstract
Semantic segmentation is a very popular topic in modern computer vision and it has applications to many fields. Researchers proposed a variety of architectures over time, but the most common ones exploit an encoder-decoder structure that aims to capture the semantics of the image and it low level features. The encoder uses convolutional layers, in general with a stride larger than one, to extract the features, while the decoder recreates the image by upsampling an using skip connections with the first layers. In this work, we use DeepLab as architecture to test the effectiveness of creating an ensemble of networks by randomly changing the activation functions inside the network multiple times. We also use different backbone networks in our DeepLab to validate our findings. We manage to reach a dice coefficient of 0.888, and a mean Intersection over Union (mIoU) of 0.825, in the competitive Kvasir-SEG dataset. Results in skin detection also confirm the performance of the proposed ensemble, which is ranked first with respect to other state-of-the-art approaches (including HardNet) in a large set of testing datasets. The developed code will be available at https://github.com/LorisNanni.
Keywords
semantic segmentation; activation function, deep ensembles
Subject
Computer Science and Mathematics, Algebra and Number Theory
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.
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