Dan Zeng

Defending Against Adversarial Examples via Soft Decision Trees Embedding

2019

Convolutional neural networks (CNNs) have shown vulnerable to adversarial examples which contain imperceptible perturbations. In this paper, we propose an approach to defend against adversarial examples with soft decision trees embedding. Firstly, we extract the …

[Temporal and spatial distribution of air pollution in Shenzhen City during 2014-2016].

2018 · PubMed

OBJECTIVE: To investigate spatial-temporal distribution characteristics of air quality indexes( AQI) in Shenzhen City and provide scientific basis for control of air pollution. METHODS: The monitoring data of AQI collected at the 19 monitoring posts …

Trustable Co-Label Learning From Multiple Noisy Annotators

2021 · IEEE Transactions on Multimedia

Supervised deep learning depends on massive accurately annotated examples, which is usually impractical in many real-world scenarios. A typical alternative is learning from multiple noisy annotators. Numerous earlier works assume that all labels are noisy, …

Model Conversion via Differentially Private Data-Free Distillation

2023 · arXiv (Cornell University)

While massive valuable deep models trained on large-scale data have been released to facilitate the artificial intelligence community, they may encounter attacks in deployment which leads to privacy leakage of training data. In this work, …

Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation

2024 · arXiv (Cornell University)

Deep learning models can achieve high inference accuracy by extracting rich knowledge from massive well-annotated data, but may pose the risk of data privacy leakage in practical deployment. In this paper, we present an effective …