小样本学习-张长水

小样本学习
2019-07-10
张长水清华大学自动化系
大眼睛实验室：
机器学习方法及其应用研究
研究方向：
•流形学习
•半监督学习
•基于间隔的学习
•多任务学习
•稀疏学习
•机器学习中的优化方法
•深度学习
•/Introduction.html •/2002.html
Deep Learning Needs a lot of Data
•Many samples
基于深度学习的交通标识识别
•实际应用
•参与中国“智能车未来挑战赛”
•无人车的一个子系统
•单张图片识别率：99.5%
路上标识识别
箭头、斑马线、停车线、
车道线
识别率：99.49%
Image Recognition
Labeling
•Crowd sourcing
图片截自12306网站
Image Recognition Data Capture
Semi-Supervised Learning •Supervised Learning
•
Unsupervised Learning
()()()1111j i t t i
ij j i x N x t t
f w f y f Wf y αααα+∈+=+−=+−∑()
()()()()0111011lim 1t t i t i t t f y
f W y W y f I W y ααααα−−+=−→∞
==+−=−−∑
Applications：Color Transfer and Background Replacment
Multi-Instance Learning
An example:
Positive bags: {1,2,3}, {2,3,7,8}, {2,6,9}
Negative bags: {3,5,6,7}
Through Multi-instance learning,
Learned Concept:{2}
Active learning
Looking for valuable data to learn.
SVM is used as a method for active learning. Version space
Multi-Label Learning
◼City
◼Street
◼Building
Annotation:
City
Car, Building, Road,
Human, Tree
City
road car building
Co-Training
•Classifier 1: content based •Classifier 2: link based
C1
C2
Others
•Sparse Learning
•Low Rank
•Transfer Learning
混叠图像分离
欠定分离
带复杂材质
真实混叠图像
分离淡入淡出图像
Few-shot Learning
◼Goal
☐A few-shot learner is expected to reach good generalization ability on novel classes by several samples per class.
Dual TriNet(ECCV2018)
Idea:Augment visual features produced by multiple layers of ResNet in semantic space(by adding noises into semantic features randomly).
Prototypical Networks(NIPS2017)☐Idea:learn a metric space in which classification can be performed by computing distances to prototype representations of each class
☐Prototype representation for each class:
☐Decision rule:
MAML(ICML2017)
☐Idea:Learn a set of good initial weights whereby the few-shot learner can reach well generalization performance within a few weight update steps on several support samples
☐meta-objective:
Image Recognition Data Capture
Our papers
•2004: ICML
•2006: ICML, CVPR
•2007: AAAI, SIGIR, CVPR, AISTATS
•2008: AAAI(3), CVPR, SIGKDD,T-KDE, T-Multimedia, T-NN, PR(5), T-PAMI(2)•2009: CVPR, IJCAI(3), T-PAMI，PR(2)
•2010: T-PAMI，PR(2)
•2011: PR
•2012: SIGKDD,NIPS
•2014: UAI
•2017: T-PAMI
•2018: NeurIPS, PR, T-NNLS。