100 citations · 216 across the 12 of their papers we have counts for
4 papers · 1 filter
Granular-ball Representation Learning for Deep CNN on Learning with Label Noise
Dawei Dai, Hao Zhu, Shuyin Xia +1
In actual scenarios, whether manually or automatically annotated, label noise is inevitably generated in the training data, which can affect the effectiveness of deep CNN models. T…
Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinement
Hao Zhu, Piotr Koniusz
Few-shot learning (FSL) is popular due to its ability to adapt to novel classes. Compared with inductive few-shot learning, transductive models typically perform better as they lev…
Migrating Face Swap to Mobile Devices: A lightweight Framework and A Supervised Training Solution
Haiming Yu, Hao Zhu, Xiangju Lu +1
Existing face swap methods rely heavily on large-scale networks for adequate capacity to generate visually plausible results, which inhibits its applications on resource-constraint…
Imbalance Robust Softmax for Deep Embeeding Learning
Hao Zhu, Yang Yuan, Guosheng Hu +2
Deep embedding learning is expected to learn a metric space in which features have smaller maximal intra-class distance than minimal inter-class distance. In recent years, one rese…