Zobrazeno 1 - 10
of 417
pro vyhledávání: '"Zhang, Xuanyu"'
Autor:
Wang, Yixuan, Luo, Xianzhen, Wei, Fuxuan, Liu, Yijun, Zhu, Qingfu, Zhang, Xuanyu, Yang, Qing, Xu, Dongliang, Che, Wanxiang
Existing speculative decoding methods typically require additional model structure and training processes to assist the model for draft token generation. This makes the migration of acceleration methods to the new model more costly and more demanding
Externí odkaz:
http://arxiv.org/abs/2406.17404
With the advent of personalized generation models, users can more readily create images resembling existing content, heightening the risk of violating portrait rights and intellectual property (IP). Traditional post-hoc detection and source-tracing m
Externí odkaz:
http://arxiv.org/abs/2405.16596
3D Gaussian Splatting (3DGS) has already become the emerging research focus in the fields of 3D scene reconstruction and novel view synthesis. Given that training a 3DGS requires a significant amount of time and computational cost, it is crucial to p
Externí odkaz:
http://arxiv.org/abs/2405.15118
This paper introduces Hierarchical Image Steganography, a novel method that enhances the security and capacity of embedding multiple images into a single container using diffusion models. HIS assigns varying levels of robustness to images based on th
Externí odkaz:
http://arxiv.org/abs/2405.11523
AI-generated video has revolutionized short video production, filmmaking, and personalized media, making video local editing an essential tool. However, this progress also blurs the line between reality and fiction, posing challenges in multimedia fo
Externí odkaz:
http://arxiv.org/abs/2404.16824
In this paper, we introduce an improved approach of speculative decoding aimed at enhancing the efficiency of serving large language models. Our method capitalizes on the strengths of two established techniques: the classic two-model speculative deco
Externí odkaz:
http://arxiv.org/abs/2403.09919
Convolutional neural networks can automatically learn features via deep network architectures and given input samples. However, robustness of obtained models may have challenges in varying scenes. Bigger differences of a network architecture are bene
Externí odkaz:
http://arxiv.org/abs/2402.15704
Autor:
Luo, Xianzhen, Zhu, Qingfu, Zhang, Zhiming, Qin, Libo, Zhang, Xuanyu, Yang, Qing, Xu, Dongliang, Che, Wanxiang
Program of Thoughts (PoT) is an approach characterized by its executable intermediate steps, which ensure the accuracy of the logical calculations in the reasoning process. Currently, PoT primarily uses Python. However, relying solely on a single lan
Externí odkaz:
http://arxiv.org/abs/2402.10691
Autor:
Zhao, Weixiang, Wang, Shilong, Hu, Yulin, Zhao, Yanyan, Qin, Bing, Zhang, Xuanyu, Yang, Qing, Xu, Dongliang, Che, Wanxiang
The continual learning (CL) ability is vital for deploying large language models (LLMs) in the dynamic world. Existing methods devise the learning module to acquire task-specific knowledge with parameter-efficient tuning (PET) block and the selection
Externí odkaz:
http://arxiv.org/abs/2401.08295
In the era where AI-generated content (AIGC) models can produce stunning and lifelike images, the lingering shadow of unauthorized reproductions and malicious tampering poses imminent threats to copyright integrity and information security. Current i
Externí odkaz:
http://arxiv.org/abs/2312.08883