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Found 9 papers, 1 papers with code
Feature transforms for image data augmentation
These novel methods are based on the Fourier Transform (FT), the Radon Transform (RT) and the Discrete Cosine Transform (DCT).
Deep ensembles in bioimage segmentation
Semantic segmentation consists in classifying each pixel of an image by assigning it to a specific label chosen from a set of all the available ones.
Gated recurrent units and temporal convolutional network for multilabel classification
Multilabel learning tackles the problem of associating a sample with multiple class labels.
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.
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.
Neural networks for Anatomical Therapeutic Chemical (ATC) classification
Motivation: Automatic Anatomical Therapeutic Chemical (ATC) classification is a critical and highly competitive area of research in bioinformatics because of its potential for expediting drug develop-ment and research.
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.
iProStruct2D: Identifying protein structural classes by deep learning via 2D representations
In this paper we address the problem of protein classification starting from a multi-view 2D representation of proteins.
