Zobrazeno 1 - 10
of 4 460
pro vyhledávání: '"REN, Jian"'
Autor:
Gong, Yifan, Zhan, Zheng, Li, Yanyu, Idelbayev, Yerlan, Zharkov, Andrey, Aberman, Kfir, Tulyakov, Sergey, Wang, Yanzhi, Ren, Jian
Good weight initialization serves as an effective measure to reduce the training cost of a deep neural network (DNN) model. The choice of how to initialize parameters is challenging and may require manual tuning, which can be time-consuming and prone
Externí odkaz:
http://arxiv.org/abs/2407.11966
Autor:
Cao, Junli, Goel, Vidit, Wang, Chaoyang, Kag, Anil, Hu, Ju, Korolev, Sergei, Jiang, Chenfanfu, Tulyakov, Sergey, Ren, Jian
Recent approaches representing 3D objects and scenes using Gaussian splats show increased rendering speed across a variety of platforms and devices. While rendering such representations is indeed extremely efficient, storing and transmitting them is
Externí odkaz:
http://arxiv.org/abs/2406.19434
Autor:
Sui, Yang, Li, Yanyu, Kag, Anil, Idelbayev, Yerlan, Cao, Junli, Hu, Ju, Sagar, Dhritiman, Yuan, Bo, Tulyakov, Sergey, Ren, Jian
Diffusion-based image generation models have achieved great success in recent years by showing the capability of synthesizing high-quality content. However, these models contain a huge number of parameters, resulting in a significantly large model si
Externí odkaz:
http://arxiv.org/abs/2406.04333
Autor:
Zhang, Zhixing, Li, Yanyu, Wu, Yushu, Xu, Yanwu, Kag, Anil, Skorokhodov, Ivan, Menapace, Willi, Siarohin, Aliaksandr, Cao, Junli, Metaxas, Dimitris, Tulyakov, Sergey, Ren, Jian
Diffusion-based video generation models have demonstrated remarkable success in obtaining high-fidelity videos through the iterative denoising process. However, these models require multiple denoising steps during sampling, resulting in high computat
Externí odkaz:
http://arxiv.org/abs/2406.04324
Quality-Diversity (QD) algorithms have emerged as a powerful optimization paradigm with the aim of generating a set of high-quality and diverse solutions. To achieve such a challenging goal, QD algorithms require maintaining a large archive and a lar
Externí odkaz:
http://arxiv.org/abs/2406.03731
Autor:
Ren, Jian, Liu, F. S., Li, Nan, Cui, Qifan, Zhao, Pinsong, Li, Yubin, Song, Qi, Yesuf, Hassen M., Zheng, Xian Zhong
The measurements of morphological indicators of galaxies are often influenced by a series of observational effects. In this study, we utilize a sample of over 800 TNG50 simulated galaxies with log($M_*$/M$_\odot$)$>9$ at $0.5
Externí odkaz:
http://arxiv.org/abs/2404.16686
Autor:
Li, Yanyu, Liu, Xian, Kag, Anil, Hu, Ju, Idelbayev, Yerlan, Sagar, Dhritiman, Wang, Yanzhi, Tulyakov, Sergey, Ren, Jian
Diffusion-based text-to-image generative models, e.g., Stable Diffusion, have revolutionized the field of content generation, enabling significant advancements in areas like image editing and video synthesis. Despite their formidable capabilities, th
Externí odkaz:
http://arxiv.org/abs/2403.18978
Autor:
Chen, Tsai-Shien, Siarohin, Aliaksandr, Menapace, Willi, Deyneka, Ekaterina, Chao, Hsiang-wei, Jeon, Byung Eun, Fang, Yuwei, Lee, Hsin-Ying, Ren, Jian, Yang, Ming-Hsuan, Tulyakov, Sergey
The quality of the data and annotation upper-bounds the quality of a downstream model. While there exist large text corpora and image-text pairs, high-quality video-text data is much harder to collect. First of all, manual labeling is more time-consu
Externí odkaz:
http://arxiv.org/abs/2402.19479
Autor:
Menapace, Willi, Siarohin, Aliaksandr, Skorokhodov, Ivan, Deyneka, Ekaterina, Chen, Tsai-Shien, Kag, Anil, Fang, Yuwei, Stoliar, Aleksei, Ricci, Elisa, Ren, Jian, Tulyakov, Sergey
Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages, the research community repurposes them to generate videos. Since video content is highly redundant, we argue that naively bringing advances
Externí odkaz:
http://arxiv.org/abs/2402.14797
Text-to-image (TTI) diffusion models have demonstrated impressive results in generating high-resolution images of complex and imaginative scenes. Recent approaches have further extended these methods with personalization techniques that allow them to
Externí odkaz:
http://arxiv.org/abs/2402.11487