26 citations · 41 across the 14 of their papers we have counts for
6 papers · 1 filter
M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy
Hansong Zhang, Shikun Li, Pengju Wang +2
Training state-of-the-art (SOTA) deep models often requires extensive data, resulting in substantial training and storage costs. To address these challenges, dataset condensation h…
Private Gradient Estimation is Useful for Generative Modeling
Bochao Liu, Pengju Wang, Weijia Guo +4
While generative models have proved successful in many domains, they may pose a privacy leakage risk in practical deployment. To address this issue, differentially private generati…
Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning
Huimin Wu, Chenyang Lei, Xiao Sun +5
Self-supervised representation learning follows a paradigm of withholding some part of the data and tasking the network to predict it from the remaining part. Among many techniques…
Deepfake Video Detection with Spatiotemporal Dropout Transformer
Daichi Zhang, Fanzhao Lin, Yingying Hua +3
While the abuse of deepfake technology has caused serious concerns recently, how to detect deepfake videos is still a challenge due to the high photo-realistic synthesis of each fr…
Sparsity Winning Twice: Better Robust Generalization from More Efficient Training
Tianlong Chen, Zhenyu Zhang, Pengjun Wang +4
Recent studies demonstrate that deep networks, even robustified by the state-of-the-art adversarial training (AT), still suffer from large robust generalization gaps, in addition t…
Interpretable Face Manipulation Detection via Feature Whitening
Yingying Hua, Daichi Zhang, Pengju Wang +1
Why should we trust the detections of deep neural networks for manipulated faces? Understanding the reasons is important for users in improving the fairness, reliability, privacy a…