Semi-Supervised Learning

1. Semi-Supervised Learning Jia-Bin Huang Virginia Tech ECE-5424G / CS-5824 Spring 2019

2. Administrative • HW 4 due April 10

3. Recommender Systems • Motivation • Problem formulation • Content-based recommendations • Collaborative filtering • Mean normalization

4. Problem motivation

5. Problem motivation

6. Optimization algorithm • Given , to learn : • Given , to learn :

7. Collaborative filtering • Given (and movie ratings), Can estimate • Given Can estimate

8. Collaborative filtering optimization objective • Given , estimate • Given , estimate

9. Collaborative filtering optimization objective • Given , estimate • Given , estimate • Minimize and simultaneously

10. Collaborative filtering optimization objective

11. Collaborative filtering algorithm • Initialize to small random values • Minimize using gradient descent (or an advanced optimization algorithm). For every • For a user with parameter and movie with (learned) feature , predict a star rating of

12. Collaborative filtering

13. Collaborative filtering • Predicted ratings: Low-rank matrix factorization

14. Finding related movies/products • For each product , we learn a feature vector : romance, : action, : comedy, … • How to find movie relate to movie ? Small movie j and I are “similar”

15. Recommender Systems • Motivation • Problem formulation • Content-based recommendations • Collaborative filtering • Mean normalization

16. Users who have not rated any movies

17. Users who have not rated any movies

18. Mean normalization For user , on movie predict: User 5 (Eve): Learn

19. Recommender Systems • Motivation • Problem formulation • Content-based recommendations • Collaborative filtering • Mean normalization

20. Review: Supervised Learning • K nearest neighbor • Linear Regression • Naïve Bayes • Logistic Regression • Support Vector Machines • Neural Networks

21. Review: Unsupervised Learning • Clustering, K-Mean • Expectation maximization • Dimensionality reduction • Anomaly detection • Recommendation system

22. Advanced Topics • Semi-supervised learning • Probabilistic graphical models • Generative models • Sequence prediction models • Deep reinforcement learning

23. Semi-supervised Learning • Motivation • Problem formulation • Consistency regularization • Entropy-based method • Pseudo-labeling

24. Semi-supervised Learning • Motivation • Problem formulation • Consistency regularization • Entropy-based method • Pseudo-labeling

25. Classic Paradigm Insufficient Nowadays • Modern applications: massive amounts of raw data. • Only a tiny fraction can be annotated by human experts Protein sequences Billions of webpages Images

26. Semi-supervised Learning

27. Active Learning

28. Semi-supervised Learning • Motivation • Problem formulation • Consistency regularization • Entropy-based method • Pseudo-labeling

29. Semi-supervised Learning Problem Formulation • Labeled data • Unlabeled data • Goal: Learn a hypothesis (e.g., a classifier) that has small error

30. Combining labeled and unlabeled data- Classical methods • Transductive SVM [Joachims ’99] • Co-training [Blum and Mitchell ’98] • Graph-based methods [Blum and Chawla ‘01] [Zhu, Ghahramani, Lafferty ‘03]

31. Transductive SVM • The separator goes through low density regions of the space / large margin

32. Transductive SVM Inputs: SVM Inputs:

33. TransductiveSVMs • First maximize margin over the labeled points • Use this to give initial labels to unlabeled points based on this separator. • Try flipping labels of unlabeled points to see if doing so can increase margin

34. Deep Semi-supervised Learning

35. Semi-supervised Learning • Motivation • Problem formulation • Consistency regularization • Entropy-based method • Pseudo-labeling

36. Stochastic Perturbations/Π-Model • Realistic perturbations of data points should not significantly change the output of

37. Temporal Ensembling

38. Mean Teacher

39. Virtual Adversarial Training

40. Semi-supervised Learning • Motivation • Problem formulation • Consistency regularization • Entropy-based method • Pseudo-labeling

41. EntMin • Encourages more confident predictions on unlabeled data.

42. Semi-supervised Learning • Motivation • Problem formulation • Consistency regularization • Entropy-based method • Pseudo-labeling

43. Comparison

44. Varying number of labels

45. Class mismatch in Labeled/Unlabeled datasets hurts the performance

46. Lessons • Standardized architecture + equal budget for tuning hyperparameters • Unlabeled data from a different class distribution not that useful • Most methods don’t work well in the very low labeled-data regime • Transferring Pre-Trained Imagenet produces lower error rate • Conclusions based on small datasets though