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Found 13 papers, 2 papers with code
High performing ensemble of convolutional neural networks for insect pest image detection
The best performing ensemble, which combined the CNNs using the different augmentation methods and the two new Adam variants proposed here, achieved state of the art on both insect data sets: 95. 52% on Deng and 73. 46% on IP102, a score on Deng that competed with human expert classifications.
Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation
The basic architecture in image segmentation consists of an encoder and a decoder: the first uses convolutional filters to extract features from the image, the second is responsible for generating the final output.
Comparison of different convolutional neural network activation functions and methods for building ensembles
Because activation functions inject different nonlinearities between layers that affect performance, varying them is one method for building robust ensembles of CNNs.
Exploiting Adam-like Optimization Algorithms to Improve the Performance of Convolutional Neural Networks
In this work, we compare Adam based variants based on the difference between the present and the past gradients, the step size is adjusted for each parameter.
Comparisons among different stochastic selection of activation layers for convolutional neural networks for healthcare
In this paper we classify biomedical images using ensembles of neural networks.
Deep learning and hand-crafted features for virus image classification
The set of handcrafted is mainly based on Local Binary Pattern variants, for each descriptor a different Support Vector Machine is trained, then the set of classifiers is combined by sum rule.
An Ensemble of Convolutional Neural Networks for Audio Classification
The best performing ensembles combining data augmentation techniques with different signal representations are compared and shown to outperform the best methods reported in the literature on these datasets.
A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images
In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature.
Data augmentation approaches for improving animal audio classification
To the best of our knowledge this is the largest study on data augmentation for CNNs in animal audio classification audio datasets using the same set of classifiers and parameters.
Audiogmenter: a MATLAB Toolbox for Audio Data Augmentation
Audio data augmentation is a key step in training deep neural networks for solving audio classification tasks.
Insect pest image detection and recognition based on bio-inspired methods
Our best ensembles reaches the state of the art accuracy on both the smaller dataset (92. 43%) and the IP102 dataset (61. 93%), approaching the performance of human experts on the smaller one.
Deep learning for Plankton and Coral Classification
We study how to create an ensemble based of different CNN models, fine tuned on several datasets with the aim of exploiting their diversity.
Ensemble of Convolutional Neural Networks Trained with Different Activation Functions
The goal of this work is to propose an ensemble of Convolutional Neural Networks trained using several different activation functions.
