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pro vyhledávání: '"Zhang,Huijie"'
Recent empirical studies have demonstrated that diffusion models can effectively learn the image distribution and generate new samples. Remarkably, these models can achieve this even with a small number of training samples despite a large image dimen
Externí odkaz:
http://arxiv.org/abs/2409.02426
Recently, diffusion models have emerged as a powerful class of generative models. Despite their success, there is still limited understanding of their semantic spaces. This makes it challenging to achieve precise and disentangled image generation wit
Externí odkaz:
http://arxiv.org/abs/2409.02374
Autor:
Zhang, Huijie, Lippert, Anja, Leonhardt, Ronny, Tolle, Tobias, Nagel3, Luise, Maric, Tomislav
A thorough understanding of media tightness in automotive electronics is crucial for ensuring more reliable and compact product designs, ultimately improving product quality. Concerning the fundamental characteristics of fluid leakage issues, the dyn
Externí odkaz:
http://arxiv.org/abs/2408.14083
The efficient and voidless filling of microcavities is of great importance for Lab-on-a-Chip applications. However, predicting whether microcavities will be filled or not under different circumstances is still difficult due to the local flow effects
Externí odkaz:
http://arxiv.org/abs/2407.18068
Autor:
Zhang, Huijie, Lippert, Anja, Leonhardt, Ronny, Tolle, Tobias, Nagel, Luise, Fricke, Mathis, Maric, Tomislav
Publikováno v:
Experiments in Fluids, 65, 95 (2024)
Preventing fluid penetration poses a challenging reliability concern in the context of power electronics, which is usually caused by unforeseen microfractures along the sealing joints. A better and more reliable product design heavily depends on the
Externí odkaz:
http://arxiv.org/abs/2403.09246
Diffusion models, emerging as powerful deep generative tools, excel in various applications. They operate through a two-steps process: introducing noise into training samples and then employing a model to convert random noise into new samples (e.g.,
Externí odkaz:
http://arxiv.org/abs/2312.09181
In this work, we investigate an intriguing and prevalent phenomenon of diffusion models which we term as "consistent model reproducibility": given the same starting noise input and a deterministic sampler, different diffusion models often yield remar
Externí odkaz:
http://arxiv.org/abs/2310.05264
Autor:
Zhang, Huijie, Opipari, Anthony, Chen, Xiaotong, Zhu, Jiyue, Yu, Zeren, Jenkins, Odest Chadwicke
Transparent objects present multiple distinct challenges to visual perception systems. First, their lack of distinguishing visual features makes transparent objects harder to detect and localize than opaque objects. Even humans find certain transpare
Externí odkaz:
http://arxiv.org/abs/2307.12400
Publikováno v:
Experimental and Computational Multiphase Flow, 2024, 6(2): 140-153
Parasitic currents still pose a significant challenge for the investigation of two-phase flow in Lab-on-Chip (LoC) applications with Volume-of-Fluid (VoF) simulations. To counter the impact of such spurious velocity fields in the vicinity of the flui
Externí odkaz:
http://arxiv.org/abs/2306.11532