Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Ren, Yuxi"'
Autor:
Ren, Yuxi, Xia, Xin, Lu, Yanzuo, Zhang, Jiacheng, Wu, Jie, Xie, Pan, Wang, Xing, Xiao, Xuefeng
Recently, a series of diffusion-aware distillation algorithms have emerged to alleviate the computational overhead associated with the multi-step inference process of Diffusion Models (DMs). Current distillation techniques often dichotomize into two
Externí odkaz:
http://arxiv.org/abs/2404.13686
Autor:
Zhang, Jiacheng, Wu, Jie, Ren, Yuxi, Xia, Xin, Kuang, Huafeng, Xie, Pan, Li, Jiashi, Xiao, Xuefeng, Zheng, Min, Fu, Lean, Li, Guanbin
Diffusion models have revolutionized the field of image generation, leading to the proliferation of high-quality models and diverse downstream applications. However, despite these significant advancements, the current competitive solutions still suff
Externí odkaz:
http://arxiv.org/abs/2404.05595
Autor:
Ren, Yuxi, Wu, Jie, Lu, Yanzuo, Kuang, Huafeng, Xia, Xin, Wang, Xionghui, Wang, Qianqian, Zhu, Yixing, Xie, Pan, Wang, Shiyin, Xiao, Xuefeng, Wang, Yitong, Zheng, Min, Fu, Lean
Recent advancements in diffusion-based generative image editing have sparked a profound revolution, reshaping the landscape of image outpainting and inpainting tasks. Despite these strides, the field grapples with inherent challenges, including: i) i
Externí odkaz:
http://arxiv.org/abs/2404.04860
Autor:
Cheng, Jiaxiang, Xie, Pan, Xia, Xin, Li, Jiashi, Wu, Jie, Ren, Yuxi, Li, Huixia, Xiao, Xuefeng, Zheng, Min, Fu, Lean
Recent advancement in text-to-image models (e.g., Stable Diffusion) and corresponding personalized technologies (e.g., DreamBooth and LoRA) enables individuals to generate high-quality and imaginative images. However, they often suffer from limitatio
Externí odkaz:
http://arxiv.org/abs/2403.02084
Autor:
Ren, Yuxi, Wu, Jie, Zhang, Peng, Zhang, Manlin, Xiao, Xuefeng, He, Qian, Wang, Rui, Zheng, Min, Pan, Xin
Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation. However, the success of these GAN models hinges on ponderous computational costs and labor-expensive training data. Current e
Externí odkaz:
http://arxiv.org/abs/2309.09310
Autor:
Zhang, Manlin, Wu, Jie, Ren, Yuxi, Li, Ming, Qin, Jie, Xiao, Xuefeng, Liu, Wei, Wang, Rui, Zheng, Min, Ma, Andy J.
Data is the cornerstone of deep learning. This paper reveals that the recently developed Diffusion Model is a scalable data engine for object detection. Existing methods for scaling up detection-oriented data often require manual collection or genera
Externí odkaz:
http://arxiv.org/abs/2309.03893
Autor:
Li, Ming, Wu, Jie, Cai, Jinhang, Qin, Jie, Ren, Yuxi, Xiao, Xuefeng, Zheng, Min, Wang, Rui, Pan, Xin
Recently, Synthetic data-based Instance Segmentation has become an exceedingly favorable optimization paradigm since it leverages simulation rendering and physics to generate high-quality image-annotation pairs. In this paper, we propose a Parallel P
Externí odkaz:
http://arxiv.org/abs/2206.10845
Generative Adversarial Networks (GANs) have witnessed prevailing success in yielding outstanding images, however, they are burdensome to deploy on resource-constrained devices due to ponderous computational costs and hulking memory usage. Although re
Externí odkaz:
http://arxiv.org/abs/2108.06908
Autor:
Ren, Yuxi, Xiao, Hang, Chong, Ben, Xia, Mengyang, Kou, Song, Xu, Aofei, Li, Jia, Liu, Jiantao, Ou, Honghui, Ren, Zhiwei, Yang, Guidong
Publikováno v:
In Applied Catalysis B: Environment and Energy 15 September 2024 353
Hierarchical Ni3S4@MoS2 nanocomposites as efficient electrocatalysts for hydrogen evolution reaction
Autor:
Ren, Yuxi, Zhu, Shengli, Liang, Yanqin, Li, Zhaoyang, Wu, Shuilin, Chang, Chuntao, Luo, Shuiyuan, Cui, Zhenduo
Publikováno v:
In Journal of Materials Science & Technology 30 December 2021 95:70-77