3 papers
cs.CV2020
MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization
Yangbin Chen, Yun Ma, Tom Ko +2
Model-Agnostic Meta-Learning (MAML) and its variants are popular few-shot classification methods. They train an initializer across a variety of sampled learning tasks (also known a…
cs.CV2019
Virtual Mixup Training for Unsupervised Domain Adaptation
Xudong Mao, Yun Ma, Zhenguo Yang +2
We study the problem of unsupervised domain adaptation which aims to adapt models trained on a labeled source domain to a completely unlabeled target domain. Recently, the cluster…
cs.CL2018
An Investigation of Few-Shot Learning in Spoken Term Classification
Yangbin Chen, Tom Ko, Lifeng Shang +3
In this paper, we investigate the feasibility of applying few-shot learning algorithms to a speech task. We formulate a user-defined scenario of spoken term classification as a few…