60 citations · 151 across the 17 of their papers we have counts for
6 papers · 1 filter
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…
Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques
JunKyu Lee, Lev Mukhanov, Amir Sabbagh Molahosseini +6
Deep learning is pervasive in our daily life, including self-driving cars, virtual assistants, social network services, healthcare services, face recognition, etc. However, deep ne…
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…
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…