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Deep Ensembles Based on Stochastic Activations for Semantic Segmentation

1
Department of Computer Science and Engineering, University of Bologna, Via dell’Università 50, 47521 Cesena, Italy
2
Department of Information Engineering, University of Padua, viale Gradenigo 6, 35122 Padua, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Giorgos Giannakakis
Signals 2021, 2(4), 820-833; https://doi.org/10.3390/signals2040047
Received: 28 July 2021 / Revised: 15 October 2021 / Accepted: 5 November 2021 / Published: 11 November 2021
Semantic segmentation is a very popular topic in modern computer vision, and it has applications in many fields. Researchers have proposed a variety of architectures for semantic image segmentation. The most common ones exploit an encoder–decoder structure that aims to capture the semantics of the image and its 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 and using skip connections with the first layers. The objective of this study is to propose a method for creating an ensemble of CNNs by enhancing diversity among networks with different activation functions. In this work, we use DeepLabV3+ as an 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 DeepLabV3+ to validate our findings. A comprehensive evaluation of the proposed approach is conducted across two different image segmentation problems: the first is from the medical field, i.e., polyp segmentation for early detection of colorectal cancer, and the second is skin detection for several different applications, including face detection, hand gesture recognition, and many others. As to the first problem, 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. The high performance of the proposed ensemble is confirmed in skin detection, where the proposed approach is ranked first concerning other state-of-the-art approaches (including HarDNet) in a large set of testing datasets. View Full-Text
Keywords: semantic segmentation; activation function; deep ensembles semantic segmentation; activation function; deep ensembles
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MDPI and ACS Style

Lumini, A.; Nanni, L.; Maguolo, G. Deep Ensembles Based on Stochastic Activations for Semantic Segmentation. Signals 2021, 2, 820-833. https://doi.org/10.3390/signals2040047

AMA Style

Lumini A, Nanni L, Maguolo G. Deep Ensembles Based on Stochastic Activations for Semantic Segmentation. Signals. 2021; 2(4):820-833. https://doi.org/10.3390/signals2040047

Chicago/Turabian Style

Lumini, Alessandra, Loris Nanni, and Gianluca Maguolo. 2021. "Deep Ensembles Based on Stochastic Activations for Semantic Segmentation" Signals 2, no. 4: 820-833. https://doi.org/10.3390/signals2040047

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