3 citations · 8 across the 12 of their papers we have counts for
4 papers · 1 filter
Optimal Watermark Generation under Type I and Type II Errors
Hengzhi He, Shirong Xu, Alexander Nemecek +3
Watermarking has recently emerged as a crucial tool for protecting the intellectual property of generative models and for distinguishing AI-generated content from human-generated d…
Human-Corrected Labels Learning: Enhancing Labels Quality via Human Correction of VLMs Discrepancies
Zhongnian Li, Lan Chen, Yixin Xu +2
Vision-Language Models (VLMs), with their powerful content generation capabilities, have been successfully applied to data annotation processes. However, the VLM-generated labels e…
A Probabilistic Perspective on Model Collapse
Shirong Xu, Hengzhi He, Guang Cheng
In recent years, model collapse has become a critical issue in language model training, making it essential to understand the underlying mechanisms driving this phenomenon. In this…
Recursive Learning Without Collapse: A Weighting-Based Stabilization Framework
Hengzhi He, Shirong Xu, Guang Cheng
Recent studies identified an intriguing phenomenon in recursive generative model training known as model collapse, where models trained on data generated by previous models exhibit…