A robust approach for deep neural networks in presence of label noise: relabelling and filtering instances during training

1 code implementation • 8 Sep 2021 • Anabel Gómez-Ríos, Julián Luengo, Francisco Herrera

This algorithm filters and relabels instances of the training set based on the predictions and their probabilities made by the backbone neural network during the training process.

Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation

no code implementations • 27 Mar 2018 • Anabel Gómez-Ríos, Siham Tabik, Julián Luengo, ASM Shihavuddin, Bartosz Krawczyk, Francisco Herrera

The recognition of coral species based on underwater texture images pose a significant difficulty for machine learning algorithms, due to the three following challenges embedded in the nature of this data: 1) datasets do not include information about the global structure of the coral; 2) several species of coral have very similar characteristics; and 3) defining the spatial borders between classes is difficult as many corals tend to appear together in groups.

Data Augmentation General Classification +1