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Found 7 papers, 0 papers with code
Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators
Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels.
Robust Learning via Golden Symmetric Loss of (un)Trusted Labels
In this paper, we propose to construct a golden symmetric loss (GSL) based on the estimated confusion matrix as to avoid overfitting to noisy labels and learn effectively from hard classes.
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models
Furthermore, relabeling only 10% of the data using the expert's results in over 90% classification accuracy with SVM.
TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise
Based on the insights, we design TrustNet that first adversely learns the pattern of noise corruption, being it both symmetric or asymmetric, from a small set of trusted data.
ExpertNet: Adversarial Learning and Recovery Against Noisy Labels
Today's available datasets in the wild, e. g., from social media and open platforms, present tremendous opportunities and challenges for deep learning, as there is a significant portion of tagged images, but often with noisy, i. e. erroneous, labels.
QActor: On-line Active Learning for Noisy Labeled Stream Data
A central feature of QActor is to dynamically adjust the query limit according to the learning loss for each data batch.
Online Label Aggregation: A Variational Bayesian Approach
Our evaluation results on various online scenarios show that BiLA can effectively infer the true labels, with an error rate reduction of at least 10 to 1. 5 percent points for synthetic and real-world datasets, respectively.
