29 citations · 104 across the 9 of their papers we have counts for
7 papers
Spectral Feature Augmentation for Graph Contrastive Learning and Beyond
Yifei Zhang, Hao Zhu, Zixing Song +2
Although augmentations (e.g., perturbation of graph edges, image crops) boost the efficiency of Contrastive Learning (CL), feature level augmentation is another plausible, compleme…
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…
Graph-adaptive Rectified Linear Unit for Graph Neural Networks
Yifei Zhang, Hao Zhu, Ziqiao Meng +2
Graph Neural Networks (GNNs) have achieved remarkable success by extending traditional convolution to learning on non-Euclidean data. The key to the GNNs is adopting the neural mes…
Contrastive Laplacian Eigenmaps
Hao Zhu, Ke Sun, Piotr Koniusz
Graph contrastive learning attracts/disperses node representations for similar/dissimilar node pairs under some notion of similarity. It may be combined with a low-dimensional embe…
REFINE: Random RangE FInder for Network Embedding
Hao Zhu, Piotr Koniusz
Network embedding approaches have recently attracted considerable interest as they learn low-dimensional vector representations of nodes. Embeddings based on the matrix factorizati…
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…