9 citations · 43 across the 14 of their papers we have counts for
14 papers
MITA: Bridging the Gap between Model and Data for Test-time Adaptation
Yige Yuan, Bingbing Xu, Teng Xiao +4
Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. However, existing mainstream TTA methods, predominantly operating at ba…
TEA: Test-time Energy Adaptation
Yige Yuan, Bingbing Xu, Liang Hou +3
Test-time adaptation (TTA) aims to improve model generalizability when test data diverges from training distribution, offering the distinct advantage of not requiring access to tra…
Layer-adaptive Structured Pruning Guided by Latency
Siyuan Pan, Linna Zhang, Jie Zhang +3
Structured pruning can simplify network architecture and improve inference speed. Combined with the underlying hardware and inference engine in which the final model is deployed, b…
PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion
Yige Yuan, Bingbing Xu, Bo Lin +4
The generalization of neural networks is a central challenge in machine learning, especially concerning the performance under distributions that differ from training ones. Current…
Graph Adversarial Immunization for Certifiable Robustness
Shuchang Tao, Huawei Shen, Qi Cao +3
Despite achieving great success, graph neural networks (GNNs) are vulnerable to adversarial attacks. Existing defenses focus on developing adversarial training or model modificatio…
Adversarial Camouflage for Node Injection Attack on Graphs
Shuchang Tao, Qi Cao, Huawei Shen +4
Node injection attacks on Graph Neural Networks (GNNs) have received increasing attention recently, due to their ability to degrade GNN performance with high attack success rates.…
Augmentation-Aware Self-Supervision for Data-Efficient GAN Training
Liang Hou, Qi Cao, Yige Yuan +7
Training generative adversarial networks (GANs) with limited data is challenging because the discriminator is prone to overfitting. Previously proposed differentiable augmentation…
Conditional GANs with Auxiliary Discriminative Classifier
Liang Hou, Qi Cao, Huawei Shen +3
Conditional generative models aim to learn the underlying joint distribution of data and labels to achieve conditional data generation. Among them, the auxiliary classifier generat…
Self-Supervised GANs with Label Augmentation
Liang Hou, Huawei Shen, Qi Cao +1
Recently, transformation-based self-supervised learning has been applied to generative adversarial networks (GANs) to mitigate catastrophic forgetting in the discriminator by intro…
SDGNN: Learning Node Representation for Signed Directed Networks
Junjie Huang, Huawei Shen, Liang Hou +1
Network embedding is aimed at mapping nodes in a network into low-dimensional vector representations. Graph Neural Networks (GNNs) have received widespread attention and lead to st…
Slimmable Generative Adversarial Networks
Liang Hou, Zehuan Yuan, Lei Huang +3
Generative adversarial networks (GANs) have achieved remarkable progress in recent years, but the continuously growing scale of models makes them challenging to deploy widely in pr…
Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification
Bingbing Xu, Junjie Huang, Liang Hou +3
Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the represe…
Adversarial Immunization for Certifiable Robustness on Graphs
Shuchang Tao, Huawei Shen, Qi Cao +2
Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning…
Signed Graph Attention Networks
Junjie Huang, Huawei Shen, Liang Hou +1
Graph or network data is ubiquitous in the real world, including social networks, information networks, traffic networks, biological networks and various technical networks. The no…