40 citations · 56 across the 5 of their papers we have counts for
3 papers · 1 filter
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-…
Semi-Automatic Data Annotation guided by Feature Space Projection
Barbara Caroline Benato, Jancarlo Ferreira Gomes, Alexandru Cristian Telea +1
Data annotation using visual inspection (supervision) of each training sample can be laborious. Interactive solutions alleviate this by helping experts propagate labels from a few…