2 citations · 3 across the 6 of their papers we have counts for
6 papers
From Seeds to Semantics: Measuring Semantic Accessibility in Deterministic Diffusion Models
Wei Wei, Yizhou Zeng, Kuntian Chen +3
Diffusion models generate samples through a sequence of learned denoising steps, and recent work has studied how semantic structure appears along this sampling process. We study th…
QoSDiff: An Implicit Topological Embedding Learning Framework Leveraging Denoising Diffusion and Adversarial Attention for Robust QoS Prediction
Guanchen Du, Jianlong Xu, Wei Wei
Accurate Quality of Service (QoS) prediction is fundamental to service computing, providing essential data-driven guidance for service selection and ensuring superior user experien…
Delta Sampling: Data-Free Knowledge Transfer Across Diffusion Models
Zhidong Gao, Zimeng Pan, Yuhang Yao +2
Diffusion models like Stable Diffusion (SD) drive a vibrant open-source ecosystem including fully fine-tuned checkpoints and parameter-efficient adapters such as LoRA, LyCORIS, and…
Enhanced Sample Selection with Confidence Tracking: Identifying Correctly Labeled yet Hard-to-Learn Samples in Noisy Data
Weiran Pan, Wei Wei, Feida Zhu +1
We propose a novel sample selection method for image classification in the presence of noisy labels. Existing methods typically consider small-loss samples as correctly labeled. Ho…
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models
Fei Deng, Qifei Wang, Wei Wei +2
Reward finetuning has emerged as a promising approach to aligning foundation models with downstream objectives. Remarkable success has been achieved in the language domain by using…
HiFi Tuner: High-Fidelity Subject-Driven Fine-Tuning for Diffusion Models
Zhonghao Wang, Wei Wei, Yang Zhao +4
This paper explores advancements in high-fidelity personalized image generation through the utilization of pre-trained text-to-image diffusion models. While previous approaches hav…