192 citations · 617 across the 27 of their papers we have counts for
5 papers · 1 filter
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning
Yunhui Guo, Yandong Li, Liqiang Wang +1
There is an increasing number of pre-trained deep neural network models. However, it is still unclear how to effectively use these models for a new task. Transfer learning, which a…
Transferring Robustness for Graph Neural Network Against Poisoning Attacks
Xianfeng Tang, Yandong Li, Yiwei Sun +3
Graph neural networks (GNNs) are widely used in many applications. However, their robustness against adversarial attacks is criticized. Prior studies show that using unnoticeable m…
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Yandong Li, Lijun Li, Liqiang Wang +2
Powerful adversarial attack methods are vital for understanding how to construct robust deep neural networks (DNNs) and for thoroughly testing defense techniques. In this paper, we…
Joint Modeling of Dense and Incomplete Trajectories for Citywide Traffic Volume Inference
Xianfeng Tang, Boqing Gong, Yanwei Yu +4
Real-time traffic volume inference is key to an intelligent city. It is a challenging task because accurate traffic volumes on the roads can only be measured at certain locations w…
Depthwise Convolution is All You Need for Learning Multiple Visual Domains
Yunhui Guo, Yandong Li, Rogerio Feris +2
There is a growing interest in designing models that can deal with images from different visual domains. If there exists a universal structure in different visual domains that can…