13 citations · 69 across the 12 of their papers we have counts for
12 papers
Feature Noise Boosts DNN Generalization under Label Noise
Lu Zeng, Xuan Chen, Xiaoshuang Shi +1
The presence of label noise in the training data has a profound impact on the generalization of deep neural networks (DNNs). In this study, we introduce and theoretically demonstra…
Exposing the Fake: Effective Diffusion-Generated Images Detection
Ruipeng Ma, Jinhao Duan, Fei Kong +2
Image synthesis has seen significant advancements with the advent of diffusion-based generative models like Denoising Diffusion Probabilistic Models (DDPM) and text-to-image diffus…
Improve Video Representation with Temporal Adversarial Augmentation
Jinhao Duan, Quanfu Fan, Hao Cheng +2
Recent works reveal that adversarial augmentation benefits the generalization of neural networks (NNs) if used in an appropriate manner. In this paper, we introduce Temporal Advers…
An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization
Fei Kong, Jinhao Duan, RuiPeng Ma +4
Recently, diffusion models have achieved remarkable success in generating tasks, including image and audio generation. However, like other generative models, diffusion models are p…
Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Concatenation
Jin Sun, Xiaoshuang Shi, Zhiyuan Wang +3
Modeling in Computer Vision has evolved to MLPs. Vision MLPs naturally lack local modeling capability, to which the simplest treatment is combined with convolutional layers. Convol…
Are Diffusion Models Vulnerable to Membership Inference Attacks?
Jinhao Duan, Fei Kong, Shiqi Wang +2
Diffusion-based generative models have shown great potential for image synthesis, but there is a lack of research on the security and privacy risks they may pose. In this paper, we…
Self-paced Resistance Learning against Overfitting on Noisy Labels
Xiaoshuang Shi, Zhenhua Guo, Kang Li +2
Noisy labels composed of correct and corrupted ones are pervasive in practice. They might significantly deteriorate the performance of convolutional neural networks (CNNs), because…
A Scalable Optimization Mechanism for Pairwise based Discrete Hashing
Xiaoshuang Shi, Fuyong Xing, Zizhao Zhang +3
Maintaining the pair similarity relationship among originally high-dimensional data into a low-dimensional binary space is a popular strategy to learn binary codes. One simiple and…
Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays
Jinzheng Cai, Le Lu, Adam P. Harrison +3
Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images. Harvesting such localizations from exi…
Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection
Zizhao Zhang, Fuyong Xing, Hai Su +2
The fast growing deep learning technologies have become the main solution of many machine learning problems for medical image analysis. Deep convolution neural networks (CNNs), as…
Revisiting Graph Construction for Fast Image Segmentation
Zizhao Zhang, Fuyong Xing, Hanzi Wang +4
In this paper, we propose a simple but effective method for fast image segmentation. We re-examine the locality-preserving character of spectral clustering by constructing a graph…
SemiContour: A Semi-supervised Learning Approach for Contour Detection
Zizhao Zhang, Fuyong Xing, Xiaoshuang Shi +1
Supervised contour detection methods usually require many labeled training images to obtain satisfactory performance. However, a large set of annotated data might be unavailable or…