40 citations · 56 across the 7 of their papers we have counts for
7 papers
Transformed Multi-view 3D Shape Features with Contrastive Learning
Márcus Vinícius Lobo Costa, Sherlon Almeida da Silva, Bárbara Caroline Benato +2
This paper addresses the challenges in representation learning of 3D shape features by investigating state-of-the-art backbones paired with both contrastive supervised and self-sup…
Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning
Bárbara Caroline Benato, Alexandre Xavier Falcão, Alexandru-Cristian Telea
Lacking supervised data is an issue while training deep neural networks (DNNs), mainly when considering medical and biological data where supervision is expensive. Recently, Embedd…
Iterative Pseudo-Labeling with Deep Feature Annotation and Confidence-Based Sampling
Barbara C Benato, Alexandru C Telea, Alexandre X Falcão
Training deep neural networks is challenging when large and annotated datasets are unavailable. Extensive manual annotation of data samples is time-consuming, expensive, and error-…
Intestinal Parasites Classification Using Deep Belief Networks
Mateus Roder, Leandro A. Passos, Luiz Carlos Felix Ribeiro +3
Currently, approximately billion people are infected by intestinal parasites worldwide. Diseases caused by such infections constitute a public health problem in most tropical c…
Convolutional Neural Networks from Image Markers
Barbara C. Benato, Italos E. de Souza, Felipe L. Galvão +1
A technique named Feature Learning from Image Markers (FLIM) was recently proposed to estimate convolutional filters, with no backpropagation, from strokes drawn by a user on very…
Semi-supervised deep learning based on label propagation in a 2D embedded space
Barbara Caroline Benato, Jancarlo Ferreira Gomes, Alexandru Cristian Telea +1
While convolutional neural networks need large labeled sets for training images, expert human supervision of such datasets can be very laborious. Proposed solutions propagate label…