139 citations · 312 across the 20 of their papers we have counts for
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Revisiting Data-Free Knowledge Distillation with Poisoned Teachers
Junyuan Hong, Yi Zeng, Shuyang Yu +3
Data-free knowledge distillation (KD) helps transfer knowledge from a pre-trained model (known as the teacher model) to a smaller model (known as the student model) without access…
Reconstructive Neuron Pruning for Backdoor Defense
Yige Li, Xixiang Lyu, Xingjun Ma +4
Deep neural networks (DNNs) have been found to be vulnerable to backdoor attacks, raising security concerns about their deployment in mission-critical applications. While existing…
Towards Adversarially Robust Continual Learning
Tao Bai, Chen Chen, Lingjuan Lyu +2
Recent studies show that models trained by continual learning can achieve the comparable performances as the standard supervised learning and the learning flexibility of continual…
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks
Yige Li, Xixiang Lyu, Nodens Koren +3
Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time attack that injects a trigger pattern into a small proportion of training data so as to contro…
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness
Lingjuan Lyu, Xuanli He, Yitong Li
It has been demonstrated that hidden representation learned by a deep model can encode private information of the input, hence can be exploited to recover such information with rea…
Collaborative Fairness in Federated Learning
Lingjuan Lyu, Xinyi Xu, Qian Wang
In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability. This problem can be addressed by Distr…