# Deep Ensembles Based on Stochastic Activations for Semantic Segmentation
###### Abstract
Again, we usually face occlusions and intra-class variance, since images have very different light and the subjects can be very different from each other. The outbreak of deep learning for computer vision led to an increased effort to improve the classification performances of methods for semantic segmentation on a variety of applications. Nowadays state-of-the-art approaches have performances comparable with human experts in polyp segmentation [2, 11, 12, 13]. However, this application has a long history, and the first methods used classic machine learning techniques. For example, [PERSON] et al. [14] trained five models for polyp segmentation including both classical machine learning techniques as well as convolutional networks. [PERSON] et al. [15] proposed a couple of fully convolutional networks to participate at the Gastrointestinal Image ANAlysis (GINAA) in 2017 and 2018 and managed to reach the first and second place in the ranking in two consecutive years. Until recently, methods for polyp segmentation were trained and tested on very small datasets, preventing the networks to generalize enough and also not allowing a good statistical significance of the results. Besides, most of the largest datasets were not publicly released [2, 11]. [PERSON] et al. [16] recently made available a new public polyp dataset, named Kvasir-SEG dataset, including 1,000 polyp images annotated by expert endoscopists from the Oslo University Hospital. [PERSON] et al. [17] propose a method based on ResNet and U-Net which reached very promising results in this novel dataset. Skin detection also saw a huge number of papers dealing with this issue both before and after the deep learning revolution. For example, [PERSON] et al. [18] proposed an approach based on classical machine learning techniques such as histograms analysis and Gaussian mixture classifiers. More recently, [PERSON] et al. [19] proposed a system for hand recognition based on skin segmentation using CNNs. [PERSON] et al. [20] used a CNN with skip connections for generic skin recognition, following the modern trends that tend to apply skip connections in neural networks more and more often. [PERSON] et al. [21] used skin recognition based on CNN to detect hands, to interpret the sign language. [PERSON] [22] performed extensive experiments using several CNN architectures and concluded that DeepLabv3+ gained the best performance for skin segmentation. In this paper, we propose a framework for designing ensemble classifiers for semantic segmentation. The main contribution of this work is:  
A method for increasing classifier diversity where each neural network is stochastically designed by varying the activation layers to increase the diversity of the ensemble. A selection procedure to include in the final set a pool of K networks selected as the most diverse networks on the given problem. The selection procedure is performed using only training data  
Extensive experimentations in two very different segmentation problems confirm the good performance of the ensemble. Our approach has been compared with other state-of-the-art methods in both problems, included the recently proposed HarDNet-MSEG [23] which here is evaluated for the first time in the skin detection problem.