Projects

Distinction Maximization Loss

Deep Learning Code. A project to improve out-of-distribution detection (open set recognition) and uncertainty estimation to your deep learning project.

Enhancing Deep Learning Performance Using Displaced Rectifier Linear Unit

Deep Learning Book. Published by Editora Dialética.

Enhancing deep learning performance using displaced rectifier linear unit

Deep Learning Masters Dissertation. Center of Informatics (CIn), Federal University of Pernambuco (UFPE).

Entropic Out-of-Distribution Detection

Deep Learning Code. A project to add scalable state-of-the-art out-of-distribution detection (open set recognition) support to your deep learning model.

SVM-CNN

Deep Learning Code. A project to build a feature extractor based on Tensorflow and Scikit-learn created to improve the accuracy of SVM to classify MNIST dataset fast and with more accuracy.

The Robust Deep Learning Library

Deep Learning Code. A project to train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances.

Towards Robust Deep Learning using Entropic Losses

Deep Learning PhD Thesis. Center of Informatics (CIn), Federal University of Pernambuco (UFPE).

A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation

International Joint Conference on Neural Networks 2020.

Additive Margin SincNet for Speaker Recognition

International Joint Conference on Neural Networks 2019.

AM-MobileNet1D: A Portable Model for Speaker Recognition

International Joint Conference on Neural Networks 2020.