Zobrazeno 1 - 10
of 848
pro vyhledávání: '"Sung Whan An"'
Autor:
Lee, Jae-Jun, Yoon, Sung Whan
Meta-learning, which pursues an effective initialization model, has emerged as a promising approach to handling unseen tasks. However, a limitation remains to be evident when a meta-learner tries to encompass a wide range of task distribution, e.g.,
Externí odkaz:
http://arxiv.org/abs/2403.06768
Autor:
Jo, Sang-Yeong, Yoon, Sung Whan
Handling out-of-distribution samples is a long-lasting challenge for deep visual models. In particular, domain generalization (DG) is one of the most relevant tasks that aims to train a model with a generalization capability on novel domains. Most ex
Externí odkaz:
http://arxiv.org/abs/2305.13046
RAN-agnostic communications can identify intrinsic features of the unknown signal without any prior knowledge, with which incompatible RANs in the same unlicensed band could achieve better coexistence performance than today's LBT-based coexistence. B
Externí odkaz:
http://arxiv.org/abs/2211.12287
Few-shot learning allows machines to classify novel classes using only a few labeled samples. Recently, few-shot segmentation aiming at semantic segmentation on low sample data has also seen great interest. In this paper, we propose a learnable modul
Externí odkaz:
http://arxiv.org/abs/2202.06498
Publikováno v:
In Expert Systems With Applications 1 March 2024 237 Part C
Publikováno v:
International Journal of Molecular Sciences, Vol 25, Iss 8, p 4442 (2024)
Stem cell therapy stands out as a promising avenue for addressing arthritis treatment. However, its therapeutic efficacy requires further enhancement. In this study, we investigated the anti-arthritogenic potential of human amniotic mesenchymal stem
Externí odkaz:
https://doaj.org/article/1a65f18119bf45f0a9cac0bf288db781
Few-shot learning allows machines to classify novel classes using only a few labeled samples. Recently, few-shot segmentation aiming at semantic segmentation on low sample data has also seen great interest. In this paper, we propose a learnable modul
Externí odkaz:
http://arxiv.org/abs/2010.11437
Publikováno v:
Proceedings of the 37th International Conference on Machine Learning (ICML) 2020, Vienna, Austria, PMLR 119
Learning novel concepts while preserving prior knowledge is a long-standing challenge in machine learning. The challenge gets greater when a novel task is given with only a few labeled examples, a problem known as incremental few-shot learning. We pr
Externí odkaz:
http://arxiv.org/abs/2003.08561
Few-shot learning aims to handle previously unseen tasks using only a small amount of new training data. In preparing (or meta-training) a few-shot learner, however, massive labeled data are necessary. In the real world, unfortunately, labeled data a
Externí odkaz:
http://arxiv.org/abs/2003.08221
Publikováno v:
In Physical Communication October 2023 60