Search Results for author: Zhijie Deng

Found 14 papers, 7 papers with code

Exploring Memorization in Adversarial Training

no code implementations3 Jun 2021 Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, Jun Zhu

In this paper, we investigate the memorization effect in adversarial training (AT) for promoting a deeper understanding of capacity, convergence, generalization, and especially robust overfitting of adversarially trained classifiers.

Accurate and Reliable Forecasting using Stochastic Differential Equations

no code implementations28 Mar 2021 Peng Cui, Zhijie Deng, WenBo Hu, Jun Zhu

It is critical yet challenging for deep learning models to properly characterize uncertainty that is pervasive in real-world environments.

Prediction Intervals

LiBRe: A Practical Bayesian Approach to Adversarial Detection

1 code implementation CVPR 2021 Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu

Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples.

Adversarial Defense

Black-box Detection of Backdoor Attacks with Limited Information and Data

no code implementations ICCV 2021 Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, Jun Zhu

Although deep neural networks (DNNs) have made rapid progress in recent years, they are vulnerable in adversarial environments.

Understanding and Exploring the Network with Stochastic Architectures

no code implementations NeurIPS 2020 Zhijie Deng, Yinpeng Dong, Shifeng Zhang, Jun Zhu

In this work, we decouple the training of a network with stochastic architectures (NSA) from NAS and provide a first systematical investigation on it as a stand-alone problem.

Neural Architecture Search

BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian Fine-tuning

1 code implementation5 Oct 2020 Zhijie Deng, Hao Zhang, Xiao Yang, Yinpeng Dong, Jun Zhu

Despite their theoretical appealingness, Bayesian neural networks (BNNs) are left behind in real-world adoption, due to persistent concerns on their scalability, accessibility, and reliability.

Variational Inference

BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayesian Fine-tuning

no code implementations28 Sep 2020 Zhijie Deng, Xiao Yang, Hao Zhang, Yinpeng Dong, Jun Zhu

Despite their theoretical appealingness, Bayesian neural networks (BNNs) are falling far behind in terms of adoption in real-world applications compared with normal NNs, mainly due to their limited scalability in training, and low fidelity in their uncertainty estimates.

Variational Inference

Adversarial Distributional Training for Robust Deep Learning

1 code implementation NeurIPS 2020 Yinpeng Dong, Zhijie Deng, Tianyu Pang, Hang Su, Jun Zhu

Adversarial training (AT) is among the most effective techniques to improve model robustness by augmenting training data with adversarial examples.

Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure

1 code implementation22 Nov 2019 Zhijie Deng, Yucen Luo, Jun Zhu, Bo Zhang

Bayesian neural networks (BNNs) augment deep networks with uncertainty quantification by Bayesian treatment of the network weights.

Bayesian Inference Neural Architecture Search +1

Deep Bayesian Structure Networks

1 code implementation25 Sep 2019 Zhijie Deng, Yucen Luo, Jun Zhu, Bo Zhang

Bayesian neural networks (BNNs) introduce uncertainty estimation to deep networks by performing Bayesian inference on network weights.

Bayesian Inference Neural Architecture Search +1

Cluster Alignment with a Teacher for Unsupervised Domain Adaptation

1 code implementation ICCV 2019 Zhijie Deng, Yucen Luo, Jun Zhu

Deep learning methods have shown promise in unsupervised domain adaptation, which aims to leverage a labeled source domain to learn a classifier for the unlabeled target domain with a different distribution.

Unsupervised Domain Adaptation

Batch Virtual Adversarial Training for Graph Convolutional Networks

no code implementations25 Feb 2019 Zhijie Deng, Yinpeng Dong, Jun Zhu

We present batch virtual adversarial training (BVAT), a novel regularization method for graph convolutional networks (GCNs).

General Classification Node Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.