294 citations · 1.1k across the 67 of their papers we have counts for
13 papers · 1 filter
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup
Siyuan Li, Zicheng Liu, Zedong Wang +3
Mixup is a well-known data-dependent augmentation technique for DNNs, consisting of two sub-tasks: mixup generation and classification. However, the recent dominant online training…
Git: Clustering Based on Graph of Intensity Topology
Zhangyang Gao, Haitao Lin, Cheng Tan +2
\textbf{A}ccuracy, \textbf{R}obustness to noises and scales, \textbf{I}nterpretability, \textbf{S}peed, and \textbf{E}asy to use (ARISE) are crucial requirements of a good clusteri…
Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks
Zihan Liu, Yun Luo, Zelin Zang +1
Gray-box graph attacks aim at disrupting the performance of the victim model by using inconspicuous attacks with limited knowledge of the victim model. The parameters of the victim…
An Empirical Study: Extensive Deep Temporal Point Process
Haitao Lin, Cheng Tan, Lirong Wu +3
Temporal point process as the stochastic process on continuous domain of time is commonly used to model the asynchronous event sequence featuring with occurrence timestamps. Thanks…
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning
Jun Xia, Lirong Wu, Ge Wang +2
Contrastive Learning (CL) has emerged as a dominant technique for unsupervised representation learning which embeds augmented versions of the anchor close to each other (positive s…
Co-learning: Learning from Noisy Labels with Self-supervision
Cheng Tan, Jun Xia, Lirong Wu +1
Noisy labels, resulting from mistakes in manual labeling or webly data collecting for supervised learning, can cause neural networks to overfit the misleading information and degra…
A Data-driven feature selection and machine-learning model benchmark for the prediction of longitudinal dispersion coefficient
Yifeng Zhao, Pei Zhang, S. A. Galindo-Torres +1
Longitudinal Dispersion(LD) is the dominant process of scalar transport in natural streams. An accurate prediction on LD coefficient(Dl) can produce a performance leap in related s…
A data-based comparative review and AI-driven symbolic model for longitudinal dispersion coefficient in natural streams
Yifeng Zhao, Zicheng Liu, Pei Zhang +2
A better understanding of dispersion in natural streams requires knowledge of longitudinal dispersion coefficient(LDC). Various methods have been proposed for predictions of LDC. T…
GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction
Lirong Wu, Haitao Lin, Zhangyang Gao +2
Recent years have witnessed great success in handling node classification tasks with Graph Neural Networks (GNNs). However, most existing GNNs are based on the assumption that node…
Exploring Localization for Self-supervised Fine-grained Contrastive Learning
Di Wu, Siyuan Li, Zelin Zang +1
Self-supervised contrastive learning has demonstrated great potential in learning visual representations. Despite their success in various downstream tasks such as image classifica…
Unsupervised Deep Manifold Attributed Graph Embedding
Zelin Zang, Siyuan Li, Di Wu +3
Unsupervised attributed graph representation learning is challenging since both structural and feature information are required to be represented in the latent space. Existing meth…
Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection
Ajian Liu, Chenxu Zhao, Zitong Yu +11
Face presentation attack detection (PAD) is essential to secure face recognition systems primarily from high-fidelity mask attacks. Most existing 3D mask PAD benchmarks suffer from…
Conditional Local Convolution for Spatio-temporal Meteorological Forecasting
Haitao Lin, Zhangyang Gao, Yongjie Xu +3
Spatio-temporal forecasting is challenging attributing to the high nonlinearity in temporal dynamics as well as complex location-characterized patterns in spatial domains, especial…