Search Results for author: Xiaoshuang Shi

Found 14 papers, 5 papers with code

ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees

no code implementations29 Jun 2024 Zhiyuan Wang, Jinhao Duan, Lu Cheng, Yue Zhang, Qingni Wang, HengTao Shen, Xiaofeng Zhu, Xiaoshuang Shi, Kaidi Xu

Uncertainty quantification (UQ) in natural language generation (NLG) tasks remains an open challenge, exacerbated by the intricate nature of the recent large language models (LLMs).

Conformal Prediction Text Generation +1

Feature Noise Boosts DNN Generalization under Label Noise

1 code implementation3 Aug 2023 Lu Zeng, Xuan Chen, Xiaoshuang Shi, Heng Tao Shen

In this study, we introduce and theoretically demonstrate a simple feature noise method, which directly adds noise to the features of training data, can enhance the generalization of DNNs under label noise.

Exposing the Fake: Effective Diffusion-Generated Images Detection

no code implementations12 Jul 2023 RuiPeng Ma, Jinhao Duan, Fei Kong, Xiaoshuang Shi, Kaidi Xu

Image synthesis has seen significant advancements with the advent of diffusion-based generative models like Denoising Diffusion Probabilistic Models (DDPM) and text-to-image diffusion models.

Denoising Image Generation

Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Concatenation

no code implementations28 May 2023 Jin Sun, Xiaoshuang Shi, Zhiyuan Wang, Kaidi Xu, Heng Tao Shen, Xiaofeng Zhu

Then, we build a pure-MLP architecture called Caterpillar by replacing the convolutional layer with the SPC module in a hybrid model of sMLPNet.

Computational Efficiency Inductive Bias

An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization

1 code implementation26 May 2023 Fei Kong, Jinhao Duan, RuiPeng Ma, HengTao Shen, Xiaofeng Zhu, Xiaoshuang Shi, Kaidi Xu

Therefore, we also explore the robustness of diffusion models to MIA in the text-to-speech (TTS) task, which is an audio generation task.

Audio Generation Inference Attack +1

Improve Video Representation with Temporal Adversarial Augmentation

no code implementations28 Apr 2023 Jinhao Duan, Quanfu Fan, Hao Cheng, Xiaoshuang Shi, Kaidi Xu

In this paper, we introduce Temporal Adversarial Augmentation (TA), a novel video augmentation technique that utilizes temporal attention.

Are Diffusion Models Vulnerable to Membership Inference Attacks?

1 code implementation2 Feb 2023 Jinhao Duan, Fei Kong, Shiqi Wang, Xiaoshuang Shi, Kaidi Xu

In this paper, we investigate the vulnerability of diffusion models to Membership Inference Attacks (MIAs), a common privacy concern.

Image Generation

Self-paced Resistance Learning against Overfitting on Noisy Labels

1 code implementation7 May 2021 Xiaoshuang Shi, Zhenhua Guo, Kang Li, Yun Liang, Xiaofeng Zhu

They might significantly deteriorate the performance of convolutional neural networks (CNNs), because CNNs are easily overfitted on corrupted labels.


Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays

no code implementations3 Jul 2018 Jinzheng Cai, Le Lu, Adam P. Harrison, Xiaoshuang Shi, Pingjun Chen, Lin Yang

Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images.

General Classification Image Classification

Recent Advances in the Applications of Convolutional Neural Networks to Medical Image Contour Detection

no code implementations24 Aug 2017 Zizhao Zhang, Fuyong Xing, Hai Su, Xiaoshuang Shi, Lin Yang

Then we review their recent applications in medical image analysis and point out limitations, with the goal to light some potential directions in medical image analysis.

Contour Detection

SemiContour: A Semi-supervised Learning Approach for Contour Detection

no code implementations CVPR 2016 Zizhao Zhang, Fuyong Xing, Xiaoshuang Shi, Lin Yang

In this paper, we investigate the usage of semi-supervised learning (SSL) to obtain competitive detection accuracy with very limited training data (three labeled images).

Contour Detection Ensemble Learning

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