Výsledky vyhledávání - "Lanqing, Hong"

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CLAD: A realistic Continual Learning benchmark for Autonomous Driving

Autor: Verwimp, Eli, Yang, Kuo, Parisot, Sarah, Lanqing, Hong, McDonagh, Steven, Pérez-Pellitero, Eduardo, De Lange, Matthias, Tuytelaars, Tinne

In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classification and object detection. The benchmark utilises SODA10M, a recently rel

Externí odkaz: http://arxiv.org/abs/2210.03482

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Re-examining Distillation For Continual Object Detection

Autor: Verwimp, Eli, Yang, Kuo, Parisot, Sarah, Lanqing, Hong, McDonagh, Steven, Pérez-Pellitero, Eduardo, De Lange, Matthias, Tuytelaars, Tinne

Training models continually to detect and classify objects, from new classes and new domains, remains an open problem. In this work, we conduct a thorough analysis of why and how object detection models forget catastrophically. We focus on distillati

Externí odkaz: http://arxiv.org/abs/2204.01407

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Akademický článek

Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing

Autor: Shengliang Cheng, Zhen Fan, Jingjing Rao, Lanqing Hong, Qicheng Huang, Ruiqiang Tao, Zhipeng Hou, Minghui Qin, Min Zeng, Xubing Lu, Guofu Zhou, Guoliang Yuan, Xingsen Gao, Jun-Ming Liu

Publikováno v: iScience, Vol 23, Iss 12, Pp 101874- (2020)

Summary: Ferroelectric synapses using polarization switching (a purely electronic switching process) to induce analog conductance change have attracted considerable interest. Here, we propose ferroelectric photovoltaic (FePV) synapses that use polari

Externí odkaz: https://doaj.org/article/ee588bab9dde4627a09703610b560bfd

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CLAD: A realistic Continual Learning benchmark for Autonomous Driving

Autor: Eli Verwimp, Kuo Yang, Sarah Parisot, Lanqing Hong, Steven McDonagh, Eduardo Pérez-Pellitero, Matthias De Lange, Tinne Tuytelaars

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e5ba898e59714f9b15a4afbf45881e5
http://arxiv.org/abs/2210.03482

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Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms

Autor: Runpeng Yu, Hong Zhu, Kaican Li, Lanqing Hong, Rui Zhang, Nanyang Ye, Shao-Lun Huang, Xiuqiang He

Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms receive increasing attention. However, OoD generalization algorithms

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49329e84010d43818ee86eaa16199aba
http://arxiv.org/abs/2206.05749

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