1 code implementation • 24 Jan 2022 • Yassir Bendou, Yuqing Hu, Raphael Lafargue, Giulia Lioi, Bastien Pasdeloup, Stéphane Pateux, Vincent Gripon
Few-shot learning aims at leveraging knowledge learned by one or more deep learning models, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available.
Ranked #1 on
Few-Shot Learning
on Mini-Imagenet 5-way (1-shot)
1 code implementation • 18 Oct 2021 • Yuqing Hu, Vincent Gripon, Stéphane Pateux
Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples.
no code implementations • 27 May 2021 • Yuqing Hu, Xiaoyuan Cheng, Suhang Wang, Jianli Chen, Tianxiang Zhao, Enyan Dai
After discussion, it is found that data-driven models integrated engineering or physical knowledge can significantly improve the urban building energy simulation.
no code implementations • 12 Jan 2021 • Mounia Hamidouche, Carlos Lassance, Yuqing Hu, Lucas Drumetz, Bastien Pasdeloup, Vincent Gripon
In machine learning, classifiers are typically susceptible to noise in the training data.
4 code implementations • 6 Jun 2020 • Yuqing Hu, Vincent Gripon, Stéphane Pateux
Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples.
1 code implementation • 27 Jan 2020 • Yuqing Hu, Vincent Gripon, Stéphane Pateux
In few-shot classification, the aim is to learn models able to discriminate classes using only a small number of labeled examples.