62 citations · 118 across the 25 of their papers we have counts for
4 papers · 1 filter
Adversarial Example Defense via Perturbation Grading Strategy
Shaowei Zhu, Wanli Lyu, Bin Li +2
Deep Neural Networks have been widely used in many fields. However, studies have shown that DNNs are easily attacked by adversarial examples, which have tiny perturbations and grea…
FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANs
Ziqiang Li, Chaoyue Wang, Heliang Zheng +2
Data-Efficient GANs (DE-GANs), which aim to learn generative models with a limited amount of training data, encounter several challenges for generating high-quality samples. Since…
Data-Efficient Backdoor Attacks
Pengfei Xia, Ziqiang Li, Wei Zhang +1
Recent studies have proven that deep neural networks are vulnerable to backdoor attacks. Specifically, by mixing a small number of poisoned samples into the training set, the behav…
A Comprehensive Survey on Data-Efficient GANs in Image Generation
Ziqiang Li, Beihao Xia, Jing Zhang +2
Generative Adversarial Networks (GANs) have achieved remarkable achievements in image synthesis. These successes of GANs rely on large scale datasets, requiring too much cost. With…