198 citations · 203 across the 7 of their papers we have counts for
4 papers · 1 filter
Towards Robust Learning with Different Label Noise Distributions
Diego Ortego, Eric Arazo, Paul Albert +2
Noisy labels are an unavoidable consequence of labeling processes and detecting them is an important step towards preventing performance degradations in Convolutional Neural Networ…
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning
Eric Arazo, Diego Ortego, Paul Albert +2
Semi-supervised learning, i.e. jointly learning from labeled and unlabeled samples, is an active research topic due to its key role on relaxing human supervision. In the context of…
On guiding video object segmentation
Diego Ortego, Kevin McGuinness, Juan C. SanMiguel +3
This paper presents a novel approach for segmenting moving objects in unconstrained environments using guided convolutional neural networks. This guiding process relies on foregrou…
Unsupervised Label Noise Modeling and Loss Correction
Eric Arazo, Diego Ortego, Paul Albert +2
Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels. When there…