An Adversarial Approach to Few-Shot Learning

MPhil Thesis Defence


Title: "An Adversarial Approach to Few-Shot Learning"

By

Mr. Ruixiang ZHANG


Abstract

Few-shot learning aims to enable machine learning models to learn new 
concepts from few labeled instances. In this thesis, we propose a 
conceptually simple and general framework called MetaGAN for few-shot 
learning problems. Most state-of-the-art few-shot classification models 
can be integrated with MetaGAN in a principled and straightforward way. By 
introducing an adversarial generator conditioned on tasks, we augment 
vanilla few-shot classification models with the ability to discriminate 
between real and fake data. We argue that this GAN-based approach can help 
few-shot classifiers to learn sharper decision boundary, which could 
generalize better. We show that with our MetaGAN framework, we can extend 
supervised few-shot learning models to naturally cope with unlabeled data. 
Different from previous work in semi-supervised few-shot learning, our 
algorithms can deal with semi-supervision at both sample-level and 
task-level. We give theoretical justifications of the strength of MetaGAN, 
and validate the effectiveness of MetaGAN on challenging few-shot image 
classification benchmarks.


Date:			Tuesday, 24 July 2018

Time:			10:00am - 12:00noon

Venue:			Room 5566
 			Lifts 27/28

Committee Members:	Dr. Yangqiu Song (Supervisor)
 			Prof. Dit-Yan Yeung (Chairperson)
 			Dr. Raymond Wong


**** ALL are Welcome ****