60 citations · 178 across the 19 of their papers we have counts for
8 papers · 1 filter
Leveraging Data Symmetries to Select an Optimal Subset of Training Data under Label Noise
Kumar Shubham, Pavan Karjol, Kiran M K +1
The performance of machine learning models often relies on large labeled datasets; however, data collected from diverse sources can contain label noise. Recent work has shown that,…
ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State
Xinshao Wang, Yang Hua, Elyor Kodirov +3
There is a family of label modification approaches including self and non-self label correction (LC), and output regularisation. They are widely used for training robust deep neura…
Not All Knowledge Is Created Equal: Mutual Distillation of Confident Knowledge
Ziyun Li, Xinshao Wang, Di Hu +4
Mutual knowledge distillation (MKD) improves a model by distilling knowledge from another model. However, \textit{not all knowledge is certain and correct}, especially under advers…
ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks
Xinshao Wang, Yang Hua, Elyor Kodirov +2
To train robust deep neural networks (DNNs), we systematically study several target modification approaches, which include output regularisation, self and non-self label correction…
Instance Cross Entropy for Deep Metric Learning
Xinshao Wang, Elyor Kodirov, Yang Hua +1
Loss functions play a crucial role in deep metric learning thus a variety of them have been proposed. Some supervise the learning process by pairwise or tripletwise similarity cons…
Derivative Manipulation for General Example Weighting
Xinshao Wang, Elyor Kodirov, Yang Hua +1
Real-world large-scale datasets usually contain noisy labels and are imbalanced. Therefore, we propose derivative manipulation (DM), a novel and general example weighting approach…
IMAE for Noise-Robust Learning: Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude's Variance Matters
Xinshao Wang, Yang Hua, Elyor Kodirov +2
In this work, we study robust deep learning against abnormal training data from the perspective of example weighting built in empirical loss functions, i.e., gradient magnitude wit…
Deep Metric Learning by Online Soft Mining and Class-Aware Attention
Xinshao Wang, Yang Hua, Elyor Kodirov +2
Deep metric learning aims to learn a deep embedding that can capture the semantic similarity of data points. Given the availability of massive training samples, deep metric learnin…