Zobrazeno 1 - 10
of 7 192
pro vyhledávání: '"In-sun Han"'
Fault detection is crucial in industrial systems to prevent failures and optimize performance by distinguishing abnormal from normal operating conditions. Data-driven methods have been gaining popularity for fault detection tasks as the amount of con
Externí odkaz:
http://arxiv.org/abs/2406.06607
Visual anomaly detection (AD) presents significant challenges due to the scarcity of anomalous data samples. While numerous works have been proposed to synthesize anomalous samples, these synthetic anomalies often lack authenticity or require extensi
Externí odkaz:
http://arxiv.org/abs/2406.01078
Autor:
Zhuang, Huiping, He, Run, Tong, Kai, Fang, Di, Sun, Han, Li, Haoran, Chen, Tianyi, Zeng, Ziqian
In this paper, we introduce analytic federated learning (AFL), a new training paradigm that brings analytical (i.e., closed-form) solutions to the federated learning (FL) community. Our AFL draws inspiration from analytic learning -- a gradient-free
Externí odkaz:
http://arxiv.org/abs/2405.16240
In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions. Generalizing to unseen multi-modal distributions poses even greater difficulties due t
Externí odkaz:
http://arxiv.org/abs/2310.19795
Domain adaptation aims to alleviate the domain shift when transferring the knowledge learned from the source domain to the target domain. Due to privacy issues, source-free domain adaptation (SFDA), where source data is unavailable during adaptation,
Externí odkaz:
http://arxiv.org/abs/2310.08928
Publikováno v:
Ziyuan Kexue, Vol 46, Iss 11, Pp 2124-2136 (2024)
[Objective] The increasingly complex and volatile international environment aggravates the instability of the global key metal supply chain. Assessing the key metal supply risks of strategic emerging industries provides an important reference for nat
Externí odkaz:
https://doaj.org/article/5eb3956d411148aca090f046ee33b8e1
Lidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. While most prevalent methods progressively downscale the 3D point clouds and camera images and then fuse the high-level features, t
Externí odkaz:
http://arxiv.org/abs/2309.11804
While originally designed for image generation, diffusion models have recently shown to provide excellent pretrained feature representations for semantic segmentation. Intrigued by this result, we set out to explore how well diffusion-pretrained repr
Externí odkaz:
http://arxiv.org/abs/2307.02138
Domain adaptive object detection aims to leverage the knowledge learned from a labeled source domain to improve the performance on an unlabeled target domain. Prior works typically require the access to the source domain data for adaptation, and the
Externí odkaz:
http://arxiv.org/abs/2306.04385
Autor:
Anurendra Kumar, Alex W. Schrader, Bhavay Aggarwal, Ali Ebrahimpour Boroojeny, Marisa Asadian, JuYeon Lee, You Jin Song, Sihai Dave Zhao, Hee-Sun Han, Saurabh Sinha
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-19 (2024)
Abstract Imaging-based spatial transcriptomics technologies such as Multiplexed error-robust fluorescence in situ hybridization (MERFISH) can capture cellular processes in unparalleled detail. However, rigorous and robust analytical tools are needed
Externí odkaz:
https://doaj.org/article/13446de0566e4ee1b0f6e281ad7e9c35