no code implementations • 6 Mar 2024 • Yingrui Ji, Yao Zhu, Zhigang Li, Jiansheng Chen, Yunlong Kong, Jingbo Chen
Our work addresses this challenge by enhancing the detection and management of OOD samples in neural networks.
no code implementations • 8 Nov 2023 • Yao Zhu, Yuefeng Chen, Wei Wang, Xiaofeng Mao, Xiu Yan, Yue Wang, Zhigang Li, Wang Lu, Jindong Wang, Xiangyang Ji
Hence, we propose fine-tuning the parameters of the attention pooling layer during the training process to encourage the model to focus on task-specific semantics.
1 code implementation • ICCV 2023 • Xiaofeng Mao, Yuefeng Chen, Yao Zhu, Da Chen, Hang Su, Rong Zhang, Hui Xue
To give a more comprehensive robustness assessment, we introduce COCO-O(ut-of-distribution), a test dataset based on COCO with 6 types of natural distribution shifts.
no code implementations • 6 Jun 2023 • Yao Zhu, Xinyu Wang, Hong-Shuo Chen, Ronald Salloum, C. -C. Jay Kuo
A novel learning solution to image steganalysis based on the green learning paradigm, called Green Steganalyzer (GS), is proposed in this work.
no code implementations • 26 May 2023 • Rui Sun, Andi Zhang, Haiming Zhang, Jinke Ren, Yao Zhu, Ruimao Zhang, Shuguang Cui, Zhen Li
Specifically, our framework consists of two components: a sample repairing module and a detection module.
Generative Adversarial Network Out-of-Distribution Detection +1
2 code implementations • CVPR 2023 • Xiaodan Li, Yuefeng Chen, Yao Zhu, Shuhui Wang, Rong Zhang, Hui Xue
We also evaluate some robust models including both adversarially trained models and other robust trained models and find that some models show worse robustness against attribute changes than vanilla models.
no code implementations • 21 Mar 2023 • Yao Zhu, Yuefeng Chen, Xiaodan Li, Rong Zhang, Xiang Tian, Bolun Zheng, Yaowu Chen
We use an encoder to map a facial image and its identity message to a cross-model adversarial example which can disrupt multiple facial manipulation systems to achieve initiative protection.
no code implementations • 30 Nov 2022 • Yao Zhu, Yuefeng Chen, Xiaodan Li, Rong Zhang, Hui Xue, Xiang Tian, Rongxin Jiang, Bolun Zheng, Yaowu Chen
Additionally, our experiments demonstrate that model selection is non-trivial for OOD detection and should be considered as an integral of the proposed method, which differs from the claim in existing works that proposed methods are universal across different models.
no code implementations • 9 Oct 2022 • Yao Zhu, Yuefeng Chen, Chuanlong Xie, Xiaodan Li, Rong Zhang, Hui Xue, Xiang Tian, Bolun Zheng, Yaowu Chen
Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios.
2 code implementations • 9 Oct 2022 • Yao Zhu, Yuefeng Chen, Xiaodan Li, Kejiang Chen, Yuan He, Xiang Tian, Bolun Zheng, Yaowu Chen, Qingming Huang
We conduct comprehensive transferable attacks against multiple DNNs to demonstrate the effectiveness of the proposed method.
1 code implementation • 16 Sep 2022 • Xiaofeng Mao, Yuefeng Chen, Ranjie Duan, Yao Zhu, Gege Qi, Shaokai Ye, Xiaodan Li, Rong Zhang, Hui Xue
For borrowing the advantage from NLP-style AT, we propose Discrete Adversarial Training (DAT).
Ranked #1 on Domain Generalization on Stylized-ImageNet
no code implementations • 7 Nov 2021 • Yao Zhu, Xinyu Wang, Hong-Shuo Chen, Ronald Salloum, C. -C. Jay Kuo
A novel method for detecting CNN-generated images, called Attentive PixelHop (or A-PixelHop), is proposed in this work.
no code implementations • ICLR 2022 • Yao Zhu, Jiacheng Sun, Zhenguo Li
Adversarial transferability enables attackers to generate adversarial examples from the source model to attack the target model, which has raised security concerns about the deployment of DNNs in practice.
no code implementations • ICCV 2021 • Yao Zhu, Jiacheng Ma, Jiacheng Sun, Zewei Chen, Rongxin Jiang, Zhenguo Li
We find that adversarial training contributes to obtaining an energy function that is flat and has low energy around the real data, which is the key for generative capability.
no code implementations • 1 Jan 2021 • Yao Zhu, Jiacheng Sun, Zewei Chen, Zhenguo Li
We justify the algorithm with a linear model that the added saliency maps pull data away from its closest decision boundary.
1 code implementation • 15 Dec 2020 • Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yingpeng Du
Besides comparing neighbor nodes when matching neighborhood, we also try to explore useful information from the connected relations.
1 code implementation • IJCNLP 2019 • Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yang song, Tao Zhang
Recently, a few methods take relation paths into consideration but pay less attention to the order of relations in paths which is important for reasoning.
Ranked #3 on Link Prediction on FB15k (MR metric)
no code implementations • 11 Nov 2018 • Yao Zhu, Saksham Suri, Pranav Kulkarni, Yueru Chen, Jiali Duan, C. -C. Jay Kuo
An interpretable generative model for handwritten digits synthesis is proposed in this work.
1 code implementation • 13 Jan 2015 • Yao Zhu, David F. Gleich
We present a parallel algorithm for the undirected $s, t$-mincut problem with floating-point valued weights.
Distributed, Parallel, and Cluster Computing Data Structures and Algorithms Numerical Analysis