23 citations · 31 across the 6 of their papers we have counts for
6 papers
Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios
Xueying Zhan, Zeyu Dai, Qingzhong Wang +4
Pool-based Active Learning (AL) has achieved great success in minimizing labeling cost by sequentially selecting informative unlabeled samples from a large unlabeled data pool and…
Saliency Attack: Towards Imperceptible Black-box Adversarial Attack
Zeyu Dai, Shengcai Liu, Ke Tang +1
Deep neural networks are vulnerable to adversarial examples, even in the black-box setting where the attacker is only accessible to the model output. Recent studies have devised ef…
Indicative Image Retrieval: Turning Blackbox Learning into Grey
Xulu Zhang, Zhenqun Yang, Hao Tian +2
Deep learning became the game changer for image retrieval soon after it was introduced. It promotes the feature extraction (by representation learning) as the core of image retriev…
Towards General Deep Leakage in Federated Learning
Jiahui Geng, Yongli Mou, Feifei Li +4
Unlike traditional central training, federated learning (FL) improves the performance of the global model by sharing and aggregating local models rather than local data to protect…
Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes
Xueying Zhan, Qing Li, Antoni B. Chan
Active learning aims to achieve greater accuracy with less training data by selecting the most useful data samples from which it learns. Single-criterion based methods (i.e., infor…
Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge
Xiu-Shen Wei, Yu-Yan Xu, Yazhou Yao +14
WebFG 2020 is an international challenge hosted by Nanjing University of Science and Technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda Uni…