44 citations · 62 across the 6 of their papers we have counts for
6 papers
Misspecified Phase Retrieval with Generative Priors
Zhaoqiang Liu, Xinshao Wang, Jiulong Liu
In this paper, we study phase retrieval under model misspecification and generative priors. In particular, we aim to estimate an -dimensional signal from i.i.d.…
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…
ID-aware Quality for Set-based Person Re-identification
Xinshao Wang, Elyor Kodirov, Yang Hua +1
Set-based person re-identification (SReID) is a matching problem that aims to verify whether two sets are of the same identity (ID). Existing SReID models typically generate a feat…
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…
Ranked List Loss for Deep Metric Learning
Xinshao Wang, Yang Hua, Elyor Kodirov +1
The objective of deep metric learning (DML) is to learn embeddings that can capture semantic similarity and dissimilarity information among data points. Existing pairwise or triple…
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…