2 citations · 2 across the 6 of their papers we have counts for
6 papers
Wide-In, Narrow-Out: Revokable Decoding for Efficient and Effective DLLMs
Feng Hong, Geng Yu, Yushi Ye +5
Diffusion Large Language Models (DLLMs) have emerged as a compelling alternative to Autoregressive models, designed for fast parallel generation. However, existing DLLMs are plague…
Adversarial Score identity Distillation: Rapidly Surpassing the Teacher in One Step
Mingyuan Zhou, Huangjie Zheng, Yi Gu +2
Score identity Distillation (SiD) is a data-free method that has achieved SOTA performance in image generation by leveraging only a pretrained diffusion model, without requiring an…
Guided Score identity Distillation for Data-Free One-Step Text-to-Image Generation
Mingyuan Zhou, Zhendong Wang, Huangjie Zheng +1
Diffusion-based text-to-image generation models trained on extensive text-image pairs have demonstrated the ability to produce photorealistic images aligned with textual descriptio…
Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation
Mingyuan Zhou, Huangjie Zheng, Zhendong Wang +2
We introduce Score identity Distillation (SiD), an innovative data-free method that distills the generative capabilities of pretrained diffusion models into a single-step generator…
GCC: Generative Calibration Clustering
Haifeng Xia, Hai Huang, Zhengming Ding
Deep clustering as an important branch of unsupervised representation learning focuses on embedding semantically similar samples into the identical feature space. This core demand…
Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Prompts
Yueqin Yin, Zhendong Wang, Yi Gu +3
In the field of large language models (LLMs), aligning models with the diverse preferences of users is a critical challenge. Direct Preference Optimization (DPO) has played a key r…