no code implementations • 4 Aug 2021 • Cosmin Octavian Pene, Amirmasoud Ghiassi, Taraneh Younesian, Robert Birke, Lydia Y. Chen
Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels.
no code implementations • 1 Jan 2021 • Amirmasoud Ghiassi, Robert Birke, Lydia Y. Chen
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.
no code implementations • 13 Nov 2020 • Taraneh Younesian, Chi Hong, Amirmasoud Ghiassi, Robert Birke, Lydia Y. Chen
Furthermore, relabeling only 10% of the data using the expert's results in over 90% classification accuracy with SVM.
no code implementations • 13 Jul 2020 • Amirmasoud Ghiassi, Taraneh Younesian, Robert Birke, Lydia Y. Chen
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.
no code implementations • 10 Jul 2020 • Amirmasoud Ghiassi, Robert Birke, Rui Han, Lydia Y. Chen
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.
no code implementations • 28 Jan 2020 • Taraneh Younesian, Zilong Zhao, Amirmasoud Ghiassi, Robert Birke, Lydia Y. Chen
A central feature of QActor is to dynamically adjust the query limit according to the learning loss for each data batch.
no code implementations • 19 Jul 2018 • Chi Hong, Amirmasoud Ghiassi, Yichi Zhou, Robert Birke, Lydia Y. Chen
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.