Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Eli Verwimp"'
Autor:
Eli Verwimp, Kuo Yang, Sarah Parisot, Lanqing Hong, Steven McDonagh, Eduardo Pérez-Pellitero, Matthias De Lange, Tinne Tuytelaars
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e5ba898e59714f9b15a4afbf45881e5
http://arxiv.org/abs/2210.03482
http://arxiv.org/abs/2210.03482
Autor:
Eli Verwimp, Kuo Yang, Sarah Parisot, Lanqing Hong, Steven McDonagh, Eduardo Pérez Pellitero, Matthias De Lange, Tinne Tuytelaars
Publikováno v:
Eli Verwimp
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e889b6627f0d1761f03fe9c37fcc80b
Learning from non-stationary data streams and overcoming catastrophic forgetting still poses a serious challenge for machine learning research. Rather than aiming to improve state-of-the-art, in this work we provide insight into the limits and merits
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f7375ba7d46b31592af5d0e7083a819e
https://lirias.kuleuven.be/handle/123456789/680964
https://lirias.kuleuven.be/handle/123456789/680964