大會現(xiàn)場黃圣君主動學(xué)習(xí)

1、Recent Progress on Active LearningSheng-Jun Huang (黃圣君)Nanjing University of Aeronautics and Astronautics2018-4-22 VALSELearning with Fewer Labeled Data2 years for 4000 sentencesin PennTreebanktime consumingonly experts can provideaccurate annotationshigh expertisebut expensiveLabeled data is import

2、ant Can we learn with fewer labeled data?2Active Learninglabeled dataquery some labelsoracle(annotator)trainmunlabeled dataGoal: train an effective mwith least labeling cost3Active LearningWhich instance to select?Informative instancesRepresentative instancesInformative & representative instances4Re

3、cent ProgressWeak supervisionThe oracle may be noisyor unavailableCost sensitiveCare the cost rather thanthe numberMdependentDifferent ms mayhave diverse needsMore Practical and More Systematic5Recent ProgressWeak supervisionThe oracle may be noisyor unavailableCost sensitiveCare the cost rather tha

4、nthe numberMdependentDifferent ms mayhave diverse needs6Active learning with Weak SupervisionCollaborative labeling from crowdsLabeler quality estimationEnsemble kernel machine classifierRobust to label noisemHua. Collaborative Active Visual Recognition from Crowds A Distributed Ensemble Approach. P

5、AMI 2018.7.Active learning with Weak SupervisionPairwise comparison from noisy labelersLeverage both types of oraclesLower querying complexity under different noise conditionsLabeling oracleComparison oraclemXu. Noise-Tolerant Interactive Learning Using Pairwise Comparisons. NIPS 2017.8Active learni

6、ng with Weak SupervisionSelf-paced active learningSelf-annotation for high-confident instancesOracle annotation for low-confident instancesLin. Active Self-Paced Learning for Cost-Effective and Progressive Face Identification. PAMI 2018.9Active learning with Weak SupervisionActive query from source

7、domainsOracle is not available in the target domainInsufficient labeled data in all domainsOracledomainadaptationSource DomainTarget DomainWang. On Gleaning Knowledge from Multiple Domains for Active Learning. IJCAI 2017.10Unlabeled data Labeled data Unlabeled data Labeled data Recent ProgressWeak s

8、upervisionThe oracle may be noisyor unavailableCost sensitiveCare the cost rather thanthe numberMdependentDifferent ms mayhave diverse needs11Cost-Sensitive Active LearningOracles are cost-sensitiveDifferent oracles have diverse pricesSelecting both instance and oracleAccurate yet cheap annotationsL

9、ow overall qualityLow priceExpert for this queryHigh overall qualityHigh priceLess familiar with itmWho is this ?Huang. Cost-Effective Active Learning from Diverse Labelers. IJCAI 2017.12.Cost-Sensitive Active LearningLabels are cost-sensitiveLabels have hierarchiesBi-objective optimization tobalanc

10、e the cost and informationYan. Cost-Effective Active Learning for Hierarchical Multi-Label Classification. IJCAI 2018.13Cost-Sensitive Active LearningLearning task is cost-sensitiveQuery the cost of predicting a specific labelGuarantee a polynomial improvement onlabel complexity for low noise caseKr

11、ishnamurthy. Active Learning for Cost-Sensitive Classification. ICML 2017.14Recent ProgressWeak supervisionThe oracle may be noisyor unavailableCost sensitiveCare the cost rather thanthe numberMdependentDifferent ms mayhave diverse needs15Active Learning with Deep MsActive madaptationA novel criteri

12、on “distinctiveness”Reuse of pre-trained mLess training datasHuang. Cost-Effective Training of Deeps with Active MAdaptatio. arXiv 2018.16Active Learning with Deep MsActive annotation with deep generative msDeep generative mto create novel instancesOracle directly annotates the decision boundaryHuijser. Active Decision Boundary Annotation with Deep Generative Ms. ICCV 2017.17Active Learning for Various ApplicationsHuman Pose Estimation Liu & Ferrari ICCV17Face Identification Lin. PAMI18Semantic