71 citations · 127 across the 10 of their papers we have counts for
10 papers
Imagine Before You Draw: Visual Prompt Engineering for Image Generation
Liyu Jia, Fengda Zhang, Jiachun Pan +7
Incorporating visual semantic representations as an intermediate step before image generation can reduce the modeling difficulty between text and images, thereby improving generati…
Asynchronous Denoising Diffusion Models for Aligning Text-to-Image Generation
Zijing Hu, Yunze Tong, Fengda Zhang +3
Diffusion models have achieved impressive results in generating high-quality images. Yet, they often struggle to faithfully align the generated images with the input prompts. This…
D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples
Zijing Hu, Fengda Zhang, Kun Kuang
The practical applications of diffusion models have been limited by the misalignment between generated images and corresponding text prompts. Recent studies have introduced direct…
Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards
Zijing Hu, Fengda Zhang, Long Chen +6
Diffusion models have achieved remarkable success in text-to-image generation. However, their practical applications are hindered by the misalignment between generated images and c…
Distributionally Generative Augmentation for Fair Facial Attribute Classification
Fengda Zhang, Qianpei He, Kun Kuang +5
Facial Attribute Classification (FAC) holds substantial promise in widespread applications. However, FAC models trained by traditional methodologies can be unfair by exhibiting acc…
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
Jiangchao Yao, Shengyu Zhang, Yang Yao +15
Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the co…
Unified Group Fairness on Federated Learning
Fengda Zhang, Kun Kuang, Yuxuan Liu +6
Federated learning (FL) has emerged as an important machine learning paradigm where a global model is trained based on the private data from distributed clients. However, most of e…
Federated Unsupervised Representation Learning
Fengda Zhang, Kun Kuang, Zhaoyang You +6
To leverage enormous unlabeled data on distributed edge devices, we formulate a new problem in federated learning called Federated Unsupervised Representation Learning (FURL) to le…
Federated Mutual Learning
Tao Shen, Jie Zhang, Xinkang Jia +6
Federated learning (FL) enables collaboratively training deep learning models on decentralized data. However, there are three types of heterogeneities in FL setting bringing about…
Evaluation Framework For Large-scale Federated Learning
Lifeng Liu, Fengda Zhang, Jun Xiao +1
Federated learning is proposed as a machine learning setting to enable distributed edge devices, such as mobile phones, to collaboratively learn a shared prediction model while kee…