Search Results for author: Bingyan Liu

Found 8 papers, 4 papers with code

BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis

no code implementations12 Nov 2023 Tingfeng Cao, Chengyu Wang, Bingyan Liu, Ziheng Wu, Jinhui Zhu, Jun Huang

Then, to ensure that our generated prompts can generate more beautiful images, we further propose a Reinforcement Learning with Visual AI Feedback technique to fine-tune our model to maximize the reward values of the generated prompts, where the reward values are calculated based on the PickScore and the Aesthetic Scores.

Prompt Engineering

No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML

1 code implementation11 Oct 2023 Ziqi Zhang, Chen Gong, Yifeng Cai, Yuanyuan Yuan, Bingyan Liu, Ding Li, Yao Guo, Xiangqun Chen

These solutions, referred to as TEE-Shielded DNN Partition (TSDP), partition a DNN model into two parts, offloading the privacy-insensitive part to the GPU while shielding the privacy-sensitive part within the TEE.

Inference Attack Membership Inference Attack

PAI-Diffusion: Constructing and Serving a Family of Open Chinese Diffusion Models for Text-to-image Synthesis on the Cloud

no code implementations11 Sep 2023 Chengyu Wang, Zhongjie Duan, Bingyan Liu, Xinyi Zou, Cen Chen, Kui Jia, Jun Huang

Text-to-image synthesis for the Chinese language poses unique challenges due to its large vocabulary size, and intricate character relationships.

Image Generation Style Transfer

Recent Advances on Federated Learning: A Systematic Survey

no code implementations3 Jan 2023 Bingyan Liu, Nuoyan Lv, Yuanchun Guo, Yawen Li

In this paper, we provide a systematic survey on federated learning, aiming to review the recent advanced federated methods and applications from different aspects.

Federated Learning Privacy Preserving

DistFL: Distribution-aware Federated Learning for Mobile Scenarios

1 code implementation22 Oct 2021 Bingyan Liu, Yifeng Cai, Ziqi Zhang, Yuanchun Li, Leye Wang, Ding Li, Yao Guo, Xiangqun Chen

Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models.

Federated Learning Privacy Preserving

ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection

1 code implementation11 Jun 2021 Yuanchun Li, Ziqi Zhang, Bingyan Liu, Ziyue Yang, Yunxin Liu

The knowledge of a deep learning model may be transferred to a student model, leading to intellectual property infringement or vulnerability propagation.

Model Compression Transfer Learning

PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization

1 code implementation2 Mar 2021 Bingyan Liu, Yao Guo, Xiangqun Chen

Based on the grouping results, PFA conducts an FL process in a group-wise way on the federated model to accomplish the adaptation.

Federated Learning Privacy Preserving

TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning

no code implementations2 Mar 2021 Bingyan Liu, Yifeng Cai, Yao Guo, Xiangqun Chen

This paper aims to improve the transfer performance from another angle - in addition to tuning the weights, we tune the structure of pre-trained models, in order to better match the target task.

Transfer Learning

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