Search Results for author: Haibing Guan

Found 15 papers, 8 papers with code

Dual Adversarial Network for Deep Active Learning

no code implementations ECCV 2020 Shuo Wang, Yuexiang Li, Kai Ma, Ruhui Ma, Haibing Guan, Yefeng Zheng

In this paper, we investigate the overlapping problem of recent uncertainty-based approaches and propose to alleviate the issue by taking representativeness into consideration.

Active Learning

FTL: A universal framework for training low-bit DNNs via Feature Transfer

no code implementations ECCV 2020 Kunyuan Du, Ya zhang, Haibing Guan, Qi Tian, Shenggan Cheng, James Lin

Compared with low-bit models trained directly, the proposed framework brings 0. 5% to 3. 4% accuracy gains to three different quantization schemes.

Quantization Transfer Learning

Exploring Diffusion Models' Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networks

no code implementations30 May 2024 Xiaoyu Wu, Jiaru Zhang, Yang Hua, Bohan Lyu, Hao Wang, Tao Song, Haibing Guan

Through this modeling, we identify the primary cause of this corruption stage: a narrowed learning distribution inherent in the nature of few-shot fine-tuning.

Variational Inference

CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion

no code implementations CVPR 2024 Xiaoyu Wu, Yang Hua, Chumeng Liang, Jiaru Zhang, Hao Wang, Tao Song, Haibing Guan

In response, we present Contrasting Gradient Inversion for Diffusion Models (CGI-DM), a novel method featuring vivid visual representations for digital copyright authentication.

Image Generation

FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy

3 code implementations1 Jul 2023 Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan

To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global head and a personalized head, respectively.

Personalized Federated Learning

Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples

2 code implementations9 Feb 2023 Chumeng Liang, Xiaoyu Wu, Yang Hua, Jiaru Zhang, Yiming Xue, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan

Recently, Diffusion Models (DMs) boost a wave in AI for Art yet raise new copyright concerns, where infringers benefit from using unauthorized paintings to train DMs to generate novel paintings in a similar style.

FedALA: Adaptive Local Aggregation for Personalized Federated Learning

2 code implementations2 Dec 2022 Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan

A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client.

Personalized Federated Learning

Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization

1 code implementation CVPR 2021 Jiaru Zhang, Yang Hua, Zhengui Xue, Tao Song, Chengyu Zheng, Ruhui Ma, Haibing Guan

Bayesian neural networks have been widely used in many applications because of the distinctive probabilistic representation framework.

Fast and Accurate Scene Parsing via Bi-direction Alignment Networks

1 code implementation25 May 2021 Yanran Wu, Xiangtai Li, Chen Shi, Yunhai Tong, Yang Hua, Tao Song, Ruhui Ma, Haibing Guan

Motivated by this, we propose a novel network by aligning two-path information into each other through a learned flow field.

Scene Parsing Semantic Segmentation

Self-Supervised Vessel Segmentation via Adversarial Learning

1 code implementation ICCV 2021 Yuxin Ma, Yang Hua, Hanming Deng, Tao Song, Hao Wang, Zhengui Xue, Heng Cao, Ruhui Ma, Haibing Guan

Vessel segmentation is critically essential for diagnosinga series of diseases, e. g., coronary artery disease and retinal disease.

Domain Adaptation Segmentation

From Quantized DNNs to Quantizable DNNs

no code implementations11 Apr 2020 Kunyuan Du, Ya zhang, Haibing Guan

This paper proposes Quantizable DNNs, a special type of DNNs that can flexibly quantize its bit-width (denoted as `bit modes' thereafter) during execution without further re-training.

A Novel Fuzzy Search Approach over Encrypted Data with Improved Accuracy and Efficiency

no code implementations27 Apr 2019 Jinkun Cao, Jinhao Zhu, Liwei Lin, Zhengui Xue, Ruhui Ma, Haibing Guan

To avoid privacy leaks, outsourced data usually is encrypted before being sent to cloud servers, which disables traditional search schemes for plain text.

Cloud Computing Diversity

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