报告题目：Semi-supervised Learning with Augmented Distribution Alignment

报告时间：2019年5月31日(周五)上午9:00

报告地点：计算机学院B403

报告人：李文

报告人单位：瑞士苏黎世联邦理工学院

报告人简介：

李文，现任瑞士苏黎世联邦理工学院计算机视觉实验室研究员。2015年获新加坡南洋理工大学博士学位，主要研究方向为计算机视觉与迁移学习，在T-PAMI、IJCV、CVPR、ICCV、ECCV等重要国际期刊和会议上发表30多篇学术论文，谷歌学术引用1600余次。

个人主页http://www.vision.ee.ethz.ch/~liwenw/

报告摘要：

In this talk, I will present a simple yet effective semi-supervised learning approach called Augmented Distribution Alignment. We observe that an essential sampling bias exists in semi-supervised learning due to the limited amount of labeled samples, which often leads to a considerable empirical distribution mismatch between labeled data and unlabeled data. To this end, we propose to align the empirical distributions of labeled and unlabeled data to alleviate the bias. On one hand, we adopt an adversarial training strategy to minimize the distribution distance between labeled and unlabeled data as inspired by domain adaptation works. On the other hand, to deal with the small sample size issue of labeled data, we also propose a simple interpolation strategy to generate pseudo training samples. Those two strategies can be easily implemented into existing deep neural networks. We demonstrate the effectiveness of our proposed approach on the benchmark SVHN and CIFAR10 datasets, on which we achieve new state-of-the-art error rates of 3.54% and 10.09%, respectively. Our code will be available at https://github.com/qinenergy/adanet.