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Unit v transfer learning

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DineshBabuAnbalagan

Transfer learning allows machine learning models to recognize and apply knowledge learned from previous tasks or domains to new related tasks or domains. It involves identifying commonalities between tasks or domains and transferring knowledge, such as feature representations or model parameters. Common transfer learning approaches include instance-based methods that reweight source domain data, feature-based methods that learn shared feature representations, and parameter-based methods that learn shared parameters across related tasks. The goal is to leverage labeled data from source tasks or domains to improve learning on a related target task or domain with few or no labels.

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Transfer Learning Part I: Overview Sinno Jialin Pan Institute for Infocomm Research (I2R), Singapore
Transfer of Learning A psychological point of view • The study of dependency of human conduct, learning or performance on prior experience. • [Thorndike and Woodworth, 1901] explored how individuals would transfer in one context to another context that share similar characteristics.  C++  Java  Maths/Physics  Computer Science/Economics
Transfer Learning In the machine learning community • The ability of a system to recognize and apply knowledge and skills learned in previous tasks to novel tasks or new domains, which share some commonality. • Given a target task, how to identify the commonality between the task and previous (source) tasks, and transfer knowledge from the previous tasks to the target one?
Fields of Transfer Learning • Transfer learning for reinforcement learning. [Taylor and Stone, Transfer Learning for Reinforcement Learning Domains: A Survey, JMLR 2009] • Transfer learning for classification and regression problems. [Pan and Yang, A Survey on Transfer Learning, IEEE TKDE 2009] Focus!
Motivating Example I: Indoor WiFi localization -30dBm -70dBm -40dBm
Indoor WiFi Localization (cont.) Training Training Localization model Test Time Period A Localization model Test Time Period B ~1.5 meters ~6 meters Time Period A Time Period A Average Error Distance S=(-37dbm, .., -77dbm), L=(1, 3) S=(-41dbm, .., -83dbm), L=(1, 4) … S=(-49dbm, .., -34dbm), L=(9, 10) S=(-61dbm, .., -28dbm), L=(15,22) S=(-37dbm, .., -77dbm), L=(1, 3) S=(-41dbm, .., -83dbm), L=(1, 4) … S=(-49dbm, .., -34dbm), L=(9, 10) S=(-61dbm, .., -28dbm), L=(15,22) S=(-37dbm, .., -77dbm) S=(-41dbm, .., -83dbm) … S=(-49dbm, .., -34dbm) S=(-61dbm, .., -28dbm) S=(-37dbm, .., -77dbm) S=(-41dbm, .., -83dbm) … S=(-49dbm, .., -34dbm) S=(-61dbm, .., -28dbm) Drop!
Indoor WiFi Localization (cont.) Training Training Test Device A Test Device B ~ 1.5 meters ~10 meters Device A Device A S=(-37dbm, .., -77dbm), L=(1, 3) S=(-41dbm, .., -83dbm), L=(1, 4) … S=(-49dbm, .., -34dbm), L=(9, 10) S=(-61dbm, .., -28dbm), L=(15,22) S=(-37dbm, .., -77dbm) S=(-41dbm, .., -83dbm) … S=(-49dbm, .., -34dbm) S=(-61dbm, .., -28dbm) S=(-37dbm, .., -77dbm) S=(-41dbm, .., -83dbm) … S=(-49dbm, .., -34dbm) S=(-61dbm, .., -28dbm) S=(-33dbm, .., -82dbm), L=(1, 3) … S=(-57dbm, .., -63dbm), L=(10, 23) Localization model Localization model Drop! Average Error Distance
Difference between Tasks/Domains 8 Time Period A Time Period B Device B Device A
Motivating Example II: Sentiment Classification
Sentiment Classification (cont.) Training Training Test Electronics Test ~ 84.6% ~72.65% Sentiment Classifier Sentiment Classifier Drop! Electronics Classification Accuracy ElectronicsDVD
Difference between Tasks/Domains 11 Electronics Video Games (1) Compact; easy to operate; very good picture quality; looks sharp! (2) A very good game! It is action packed and full of excitement. I am very much hooked on this game. (3) I purchased this unit from Circuit City and I was very excited about the quality of the picture. It is really nice and sharp. (4) Very realistic shooting action and good plots. We played this and were hooked. (5) It is also quite blurry in very dark settings. I will never buy HP again. (6) The game is so boring. I am extremely unhappy and will probably never buy UbiSoft again.
A Major Assumption Training and future (test) data come from a same task and a same domain. Represented in same feature and label spaces. Follow a same distribution.
Training The Goal of Transfer Learning Training Classification or Regression Models DVD Source Tasks/Domains Device B Time Period B A few labeled training data Electronics Time Period A Device A Electronics Time Period A Device A Target Task/Domain Target Task/Domain
Notations Domain: Task:
Transfer Learning Heterogeneous Transfer Learning Transfer learning settings Inductive Transfer Learning Feature space Heterogeneous Tasks Single-Task Transfer Learning Identical Homogeneous Different Domain Adaption Sample Selection Bias / Covariate Shift Multi-Task Learning Domain difference is caused by feature representations Domain difference is caused by sample bias Tasks are learned simultaneously Focus on optimizing a target task
Inductive Transfer Learning Tasks Single-Task Transfer Learning Identical Different Domain Adaption Sample Selection Bias / Covariate Shift Multi-Task Learning Domain difference is caused by feature representations Domain difference is caused by sample bias Tasks are learned simultaneously Focus on optimizing a target task Assumption
Single-Task Transfer Learning Case 1 Case 2 Domain Adaption in NLP Sample Selection Bias / Covariate Shift Instance-based Transfer Learning Approaches Feature-based Transfer Learning Approaches
Single-Task Transfer Learning Case 1 Sample Selection Bias / Covariate Shift Instance-based Transfer Learning Approaches Problem Setting Assumption
Single-Task Transfer Learning Instance-based Approaches Recall, given a target task,
Single-Task Transfer Learning Instance-based Approaches (cont.)
Single-Task Transfer Learning Instance-based Approaches (cont.) Assumption:
Single-Task Transfer Learning Instance-based Approaches (cont.) Sample Selection Bias / Covariate Shift [Quionero-Candela, etal, Data Shift in Machine Learning, MIT Press 2009]
Single-Task Transfer Learning Feature-based Approaches Case 2 Problem Setting Explicit/Implicit Assumption
Single-Task Transfer Learning Feature-based Approaches (cont.) How to learn ?  Solution 1: Encode domain knowledge to learn the transformation.  Solution 2: Learn the transformation by designing objective functions to minimize difference directly.
25 Single-Task Transfer Learning Solution 1: Encode domain knowledge to learn the transformation Electronics Video Games (1) Compact; easy to operate; very good picture quality; looks sharp! (2) A very good game! It is action packed and full of excitement. I am very much hooked on this game. (3) I purchased this unit from Circuit City and I was very excited about the quality of the picture. It is really nice and sharp. (4) Very realistic shooting action and good plots. We played this and were hooked. (5) It is also quite blurry in very dark settings. I will never_buy HP again. (6) The game is so boring. I am extremely unhappy and will probably never_buy UbiSoft again.
Common features 26 Single-Task Transfer Learning Solution 1: Encode domain knowledge to learn the transformation (cont.) boring realistic hooked blurry sharp compact never_buy good exciting Video game domain specific features Electronics Domain specific features blurry never_buy boring exciting sharp hooked compact realisticgood
Single-Task Transfer Learning Solution 1: Encode domain knowledge to learn the transformation (cont.)  How to select good pivot features is an open problem.  Mutual Information on source domain labeled data  Term frequency on both source and target domain data.  How to estimate correlations between pivot and domain specific features?  Structural Correspondence Learning (SCL) [Biltzer etal. 2006]  Spectral Feature Alignment (SFA) [Pan etal. 2010] 27
Single-Task Transfer Learning Solution 2: learning the transformation without domain knowledge TargetSource Latent factors Temperature Signal properties Building structure Power of APs
Single-Task Transfer Learning Solution 2: learning the transformation without domain knowledge TargetSource Latent factors Temperature Signal properties Building structure Power of APs Cause the data distributions between domains different
Single-Task Transfer Learning Solution 2: learning the transformation without domain knowledge (cont.) TargetSource Signal properties Principal components Noisy component Building structure
Single-Task Transfer Learning Solution 2: learning the transformation without domain knowledge (cont.) Learning by only minimizing distance between distributions may map the data to noisy factors. 31
Single-Task Transfer Learning Transfer Component Analysis [Pan etal., 2009] Main idea: the learned should map the source and target domain data to the latent space spanned by the factors which can reduce domain difference and preserve original data structure. 32 High level optimization problem
Single-Task Transfer Learning Maximum Mean Discrepancy (MMD) [Alex Smola, Arthur Gretton and Kenji Kukumizu, ICML-08 tutorial]
Single-Task Transfer Learning Transfer Component Analysis (cont.)
Single-Task Transfer Learning Transfer Component Analysis (cont.)
Single-Task Transfer Learning Transfer Component Analysis (cont.) To maximize the data variance To minimize the distance between domains To preserve the local geometric structure  It is a SDP problem, expensive!  It is transductive, cannot generalize on unseen instances!  PCA is post-processed on the learned kernel matrix, which may potentially discard useful information. [Pan etal., 2008]
Single-Task Transfer Learning Transfer Component Analysis (cont.) Empirical kernel map Resultant parametric kernel Out-of-sample kernel evaluation
Single-Task Transfer Learning Transfer Component Analysis (cont.) To minimize the distance between domains Regularization on W To maximize the data variance
Inductive Transfer Learning Tasks Single-Task Transfer Learning Identical Different Domain Adaption Sample Selection Bias / Covariate Shift Multi-Task Learning Domain difference is caused by feature representations Domain difference is caused by sample bias Tasks are learned simultaneously Focus on optimizing a target task Inductive Transfer Learning Multi-Task Learning Tasks are learned simultaneously Focus on optimizing a target task Assumption Problem Setting
Inductive Transfer Learning Instance-based Transfer Learning Approaches Feature-based Transfer Learning Approaches Parameter-based Transfer Learning Approaches Modified from Multi-Task Learning Methods Target-Task-Driven Transfer Learning Methods Self-Taught Learning Methods
Inductive Transfer Learning Multi-Task Learning Methods Feature-based Transfer Learning Approaches Parameter-based Transfer Learning Approaches Modified from Multi-Task Learning Methods Setting
Inductive Transfer Learning Multi-Task Learning Methods Recall that for each task (source or target) Tasks are learned independently Motivation of Multi-Task Learning: Can the related tasks be learned jointly? Which kind of commonality can be used across tasks?
Inductive Transfer Learning Multi-Task Learning Methods -- Parameter-based approaches Assumption: If tasks are related, they should share similar parameter vectors. For example [Evgeniou and Pontil, 2004] Common part Specific part for individual task
Inductive Transfer Learning Multi-Task Learning Methods -- Parameter-based approaches (cont.)
Inductive Transfer Learning Multi-Task Learning Methods -- Parameter-based approaches (summary) A general framework: [Zhang and Yeung, 2010] [Saha etal, 2010]
Inductive Transfer Learning Multi-Task Learning Methods -- Feature-based approaches Assumption: If tasks are related, they should share some good common features. Goal: Learn a low-dimensional representation shared across related tasks.
Inductive Transfer Learning Multi-Task Learning Methods -- Feature-based approaches (cont.) [Argyriou etal., 2007]
Inductive Transfer Learning Multi-Task Learning Methods -- Feature-based approaches (cont.) Illustration
Inductive Transfer Learning Multi-Task Learning Methods -- Feature-based approaches (cont.)
Inductive Transfer Learning Multi-Task Learning Methods -- Feature-based approaches (cont.) [Ando and Zhang, 2005] [Ji etal, 2008]
Inductive Transfer Learning Instance-based Transfer Learning Approaches Feature-based Transfer Learning Approaches Target-Task-Driven Transfer Learning Methods Self-Taught Learning Methods
Inductive Transfer Learning Self-taught Learning Methods -- Feature-based approaches Motivation: There exist some higher-level features that can help the target learning task even only a few labeled data are given. Steps: 1, Learn higher-level features from a lot of unlabeled data from the source tasks. 2, Use the learned higher-level features to represent the data of the target task. 3, Training models from the new representations of the target task with corresponding labels.
Inductive Transfer Learning Self-taught Learning Methods -- Feature-based approaches (cont.) Higher-level feature construction Solution 1: Sparse Coding [Raina etal., 2007] Solution 2: Deep learning [Glorot etal., 2011]
Inductive Transfer Learning Target-Task-Driven Methods -- Instance-based approaches Intuition Assumption Part of the labeled data from the source domain can be reused after re-weighting Main Idea TrAdaBoost [Dai etal 2007] For each boosting iteration,  Use the same strategy as AdaBoost to update the weights of target domain data.  Propose a new mechanism to decrease the weights of misclassified source domain data.
Summary Inductive Transfer Learning Tasks Single-Task Transfer Learning Identical Different Feature-based Transfer Learning Approaches Instance-based Transfer Learning Approaches Parameter-based Transfer Learning Approaches Feature-based Transfer Learning Approaches Instance-based Transfer Learning Approaches
Some Research Issues  How to avoid negative transfer? Given a target domain/task, how to find source domains/tasks to ensure positive transfer.  Transfer learning meets active learning  Given a specific application, which kind of transfer learning methods should be used.
Reference  [Thorndike and Woodworth, The Influence of Improvement in one mental function upon the efficiency of the other functions, 1901]  [Taylor and Stone, Transfer Learning for Reinforcement Learning Domains: A Survey, JMLR 2009]  [Pan and Yang, A Survey on Transfer Learning, IEEE TKDE 2009]  [Quionero-Candela, etal, Data Shift in Machine Learning, MIT Press 2009]  [Biltzer etal.. Domain Adaptation with Structural Correspondence Learning, EMNLP 2006]  [Pan etal., Cross-Domain Sentiment Classification via Spectral Feature Alignment, WWW 2010]  [Pan etal., Transfer Learning via Dimensionality Reduction, AAAI 2008]
Reference (cont.)  [Pan etal., Domain Adaptation via Transfer Component Analysis, IJCAI 2009]  [Evgeniou and Pontil, Regularized Multi-Task Learning, KDD 2004]  [Zhang and Yeung, A Convex Formulation for Learning Task Relationships in Multi-Task Learning, UAI 2010]  [Saha etal, Learning Multiple Tasks using Manifold Regularization, NIPS 2010]  [Argyriou etal., Multi-Task Feature Learning, NIPS 2007]  [Ando and Zhang, A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data, JMLR 2005]  [Ji etal, Extracting Shared Subspace for Multi-label Classification, KDD 2008]
Reference (cont.)  [Raina etal., Self-taught Learning: Transfer Learning from Unlabeled Data, ICML 2007]  [Dai etal., Boosting for Transfer Learning, ICML 2007]  [Glorot etal., Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach, ICML 2011]
Thank You

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Unit v transfer learning

  • 1. Transfer Learning Part I: Overview Sinno Jialin Pan Institute for Infocomm Research (I2R), Singapore
  • 2. Transfer of Learning A psychological point of view • The study of dependency of human conduct, learning or performance on prior experience. • [Thorndike and Woodworth, 1901] explored how individuals would transfer in one context to another context that share similar characteristics.  C++  Java  Maths/Physics  Computer Science/Economics
  • 3. Transfer Learning In the machine learning community • The ability of a system to recognize and apply knowledge and skills learned in previous tasks to novel tasks or new domains, which share some commonality. • Given a target task, how to identify the commonality between the task and previous (source) tasks, and transfer knowledge from the previous tasks to the target one?
  • 4. Fields of Transfer Learning • Transfer learning for reinforcement learning. [Taylor and Stone, Transfer Learning for Reinforcement Learning Domains: A Survey, JMLR 2009] • Transfer learning for classification and regression problems. [Pan and Yang, A Survey on Transfer Learning, IEEE TKDE 2009] Focus!
  • 5. Motivating Example I: Indoor WiFi localization -30dBm -70dBm -40dBm
  • 6. Indoor WiFi Localization (cont.) Training Training Localization model Test Time Period A Localization model Test Time Period B ~1.5 meters ~6 meters Time Period A Time Period A Average Error Distance S=(-37dbm, .., -77dbm), L=(1, 3) S=(-41dbm, .., -83dbm), L=(1, 4) … S=(-49dbm, .., -34dbm), L=(9, 10) S=(-61dbm, .., -28dbm), L=(15,22) S=(-37dbm, .., -77dbm), L=(1, 3) S=(-41dbm, .., -83dbm), L=(1, 4) … S=(-49dbm, .., -34dbm), L=(9, 10) S=(-61dbm, .., -28dbm), L=(15,22) S=(-37dbm, .., -77dbm) S=(-41dbm, .., -83dbm) … S=(-49dbm, .., -34dbm) S=(-61dbm, .., -28dbm) S=(-37dbm, .., -77dbm) S=(-41dbm, .., -83dbm) … S=(-49dbm, .., -34dbm) S=(-61dbm, .., -28dbm) Drop!
  • 7. Indoor WiFi Localization (cont.) Training Training Test Device A Test Device B ~ 1.5 meters ~10 meters Device A Device A S=(-37dbm, .., -77dbm), L=(1, 3) S=(-41dbm, .., -83dbm), L=(1, 4) … S=(-49dbm, .., -34dbm), L=(9, 10) S=(-61dbm, .., -28dbm), L=(15,22) S=(-37dbm, .., -77dbm) S=(-41dbm, .., -83dbm) … S=(-49dbm, .., -34dbm) S=(-61dbm, .., -28dbm) S=(-37dbm, .., -77dbm) S=(-41dbm, .., -83dbm) … S=(-49dbm, .., -34dbm) S=(-61dbm, .., -28dbm) S=(-33dbm, .., -82dbm), L=(1, 3) … S=(-57dbm, .., -63dbm), L=(10, 23) Localization model Localization model Drop! Average Error Distance
  • 8. Difference between Tasks/Domains 8 Time Period A Time Period B Device B Device A
  • 10. Sentiment Classification (cont.) Training Training Test Electronics Test ~ 84.6% ~72.65% Sentiment Classifier Sentiment Classifier Drop! Electronics Classification Accuracy ElectronicsDVD
  • 11. Difference between Tasks/Domains 11 Electronics Video Games (1) Compact; easy to operate; very good picture quality; looks sharp! (2) A very good game! It is action packed and full of excitement. I am very much hooked on this game. (3) I purchased this unit from Circuit City and I was very excited about the quality of the picture. It is really nice and sharp. (4) Very realistic shooting action and good plots. We played this and were hooked. (5) It is also quite blurry in very dark settings. I will never buy HP again. (6) The game is so boring. I am extremely unhappy and will probably never buy UbiSoft again.
  • 12. A Major Assumption Training and future (test) data come from a same task and a same domain. Represented in same feature and label spaces. Follow a same distribution.
  • 13. Training The Goal of Transfer Learning Training Classification or Regression Models DVD Source Tasks/Domains Device B Time Period B A few labeled training data Electronics Time Period A Device A Electronics Time Period A Device A Target Task/Domain Target Task/Domain
  • 15. Transfer Learning Heterogeneous Transfer Learning Transfer learning settings Inductive Transfer Learning Feature space Heterogeneous Tasks Single-Task Transfer Learning Identical Homogeneous Different Domain Adaption Sample Selection Bias / Covariate Shift Multi-Task Learning Domain difference is caused by feature representations Domain difference is caused by sample bias Tasks are learned simultaneously Focus on optimizing a target task
  • 16. Inductive Transfer Learning Tasks Single-Task Transfer Learning Identical Different Domain Adaption Sample Selection Bias / Covariate Shift Multi-Task Learning Domain difference is caused by feature representations Domain difference is caused by sample bias Tasks are learned simultaneously Focus on optimizing a target task Assumption
  • 17. Single-Task Transfer Learning Case 1 Case 2 Domain Adaption in NLP Sample Selection Bias / Covariate Shift Instance-based Transfer Learning Approaches Feature-based Transfer Learning Approaches
  • 18. Single-Task Transfer Learning Case 1 Sample Selection Bias / Covariate Shift Instance-based Transfer Learning Approaches Problem Setting Assumption
  • 19. Single-Task Transfer Learning Instance-based Approaches Recall, given a target task,
  • 21. Single-Task Transfer Learning Instance-based Approaches (cont.) Assumption:
  • 22. Single-Task Transfer Learning Instance-based Approaches (cont.) Sample Selection Bias / Covariate Shift [Quionero-Candela, etal, Data Shift in Machine Learning, MIT Press 2009]
  • 23. Single-Task Transfer Learning Feature-based Approaches Case 2 Problem Setting Explicit/Implicit Assumption
  • 24. Single-Task Transfer Learning Feature-based Approaches (cont.) How to learn ?  Solution 1: Encode domain knowledge to learn the transformation.  Solution 2: Learn the transformation by designing objective functions to minimize difference directly.
  • 25. 25 Single-Task Transfer Learning Solution 1: Encode domain knowledge to learn the transformation Electronics Video Games (1) Compact; easy to operate; very good picture quality; looks sharp! (2) A very good game! It is action packed and full of excitement. I am very much hooked on this game. (3) I purchased this unit from Circuit City and I was very excited about the quality of the picture. It is really nice and sharp. (4) Very realistic shooting action and good plots. We played this and were hooked. (5) It is also quite blurry in very dark settings. I will never_buy HP again. (6) The game is so boring. I am extremely unhappy and will probably never_buy UbiSoft again.
  • 26. Common features 26 Single-Task Transfer Learning Solution 1: Encode domain knowledge to learn the transformation (cont.) boring realistic hooked blurry sharp compact never_buy good exciting Video game domain specific features Electronics Domain specific features blurry never_buy boring exciting sharp hooked compact realisticgood
  • 27. Single-Task Transfer Learning Solution 1: Encode domain knowledge to learn the transformation (cont.)  How to select good pivot features is an open problem.  Mutual Information on source domain labeled data  Term frequency on both source and target domain data.  How to estimate correlations between pivot and domain specific features?  Structural Correspondence Learning (SCL) [Biltzer etal. 2006]  Spectral Feature Alignment (SFA) [Pan etal. 2010] 27
  • 28. Single-Task Transfer Learning Solution 2: learning the transformation without domain knowledge TargetSource Latent factors Temperature Signal properties Building structure Power of APs
  • 29. Single-Task Transfer Learning Solution 2: learning the transformation without domain knowledge TargetSource Latent factors Temperature Signal properties Building structure Power of APs Cause the data distributions between domains different
  • 30. Single-Task Transfer Learning Solution 2: learning the transformation without domain knowledge (cont.) TargetSource Signal properties Principal components Noisy component Building structure
  • 31. Single-Task Transfer Learning Solution 2: learning the transformation without domain knowledge (cont.) Learning by only minimizing distance between distributions may map the data to noisy factors. 31
  • 32. Single-Task Transfer Learning Transfer Component Analysis [Pan etal., 2009] Main idea: the learned should map the source and target domain data to the latent space spanned by the factors which can reduce domain difference and preserve original data structure. 32 High level optimization problem
  • 33. Single-Task Transfer Learning Maximum Mean Discrepancy (MMD) [Alex Smola, Arthur Gretton and Kenji Kukumizu, ICML-08 tutorial]
  • 34. Single-Task Transfer Learning Transfer Component Analysis (cont.)
  • 35. Single-Task Transfer Learning Transfer Component Analysis (cont.)
  • 36. Single-Task Transfer Learning Transfer Component Analysis (cont.) To maximize the data variance To minimize the distance between domains To preserve the local geometric structure  It is a SDP problem, expensive!  It is transductive, cannot generalize on unseen instances!  PCA is post-processed on the learned kernel matrix, which may potentially discard useful information. [Pan etal., 2008]
  • 37. Single-Task Transfer Learning Transfer Component Analysis (cont.) Empirical kernel map Resultant parametric kernel Out-of-sample kernel evaluation
  • 38. Single-Task Transfer Learning Transfer Component Analysis (cont.) To minimize the distance between domains Regularization on W To maximize the data variance
  • 39. Inductive Transfer Learning Tasks Single-Task Transfer Learning Identical Different Domain Adaption Sample Selection Bias / Covariate Shift Multi-Task Learning Domain difference is caused by feature representations Domain difference is caused by sample bias Tasks are learned simultaneously Focus on optimizing a target task Inductive Transfer Learning Multi-Task Learning Tasks are learned simultaneously Focus on optimizing a target task Assumption Problem Setting
  • 40. Inductive Transfer Learning Instance-based Transfer Learning Approaches Feature-based Transfer Learning Approaches Parameter-based Transfer Learning Approaches Modified from Multi-Task Learning Methods Target-Task-Driven Transfer Learning Methods Self-Taught Learning Methods
  • 41. Inductive Transfer Learning Multi-Task Learning Methods Feature-based Transfer Learning Approaches Parameter-based Transfer Learning Approaches Modified from Multi-Task Learning Methods Setting
  • 42. Inductive Transfer Learning Multi-Task Learning Methods Recall that for each task (source or target) Tasks are learned independently Motivation of Multi-Task Learning: Can the related tasks be learned jointly? Which kind of commonality can be used across tasks?
  • 43. Inductive Transfer Learning Multi-Task Learning Methods -- Parameter-based approaches Assumption: If tasks are related, they should share similar parameter vectors. For example [Evgeniou and Pontil, 2004] Common part Specific part for individual task
  • 44. Inductive Transfer Learning Multi-Task Learning Methods -- Parameter-based approaches (cont.)
  • 45. Inductive Transfer Learning Multi-Task Learning Methods -- Parameter-based approaches (summary) A general framework: [Zhang and Yeung, 2010] [Saha etal, 2010]
  • 46. Inductive Transfer Learning Multi-Task Learning Methods -- Feature-based approaches Assumption: If tasks are related, they should share some good common features. Goal: Learn a low-dimensional representation shared across related tasks.
  • 47. Inductive Transfer Learning Multi-Task Learning Methods -- Feature-based approaches (cont.) [Argyriou etal., 2007]
  • 48. Inductive Transfer Learning Multi-Task Learning Methods -- Feature-based approaches (cont.) Illustration
  • 49. Inductive Transfer Learning Multi-Task Learning Methods -- Feature-based approaches (cont.)
  • 50. Inductive Transfer Learning Multi-Task Learning Methods -- Feature-based approaches (cont.) [Ando and Zhang, 2005] [Ji etal, 2008]
  • 51. Inductive Transfer Learning Instance-based Transfer Learning Approaches Feature-based Transfer Learning Approaches Target-Task-Driven Transfer Learning Methods Self-Taught Learning Methods
  • 52. Inductive Transfer Learning Self-taught Learning Methods -- Feature-based approaches Motivation: There exist some higher-level features that can help the target learning task even only a few labeled data are given. Steps: 1, Learn higher-level features from a lot of unlabeled data from the source tasks. 2, Use the learned higher-level features to represent the data of the target task. 3, Training models from the new representations of the target task with corresponding labels.
  • 53. Inductive Transfer Learning Self-taught Learning Methods -- Feature-based approaches (cont.) Higher-level feature construction Solution 1: Sparse Coding [Raina etal., 2007] Solution 2: Deep learning [Glorot etal., 2011]
  • 54. Inductive Transfer Learning Target-Task-Driven Methods -- Instance-based approaches Intuition Assumption Part of the labeled data from the source domain can be reused after re-weighting Main Idea TrAdaBoost [Dai etal 2007] For each boosting iteration,  Use the same strategy as AdaBoost to update the weights of target domain data.  Propose a new mechanism to decrease the weights of misclassified source domain data.
  • 55. Summary Inductive Transfer Learning Tasks Single-Task Transfer Learning Identical Different Feature-based Transfer Learning Approaches Instance-based Transfer Learning Approaches Parameter-based Transfer Learning Approaches Feature-based Transfer Learning Approaches Instance-based Transfer Learning Approaches
  • 56. Some Research Issues  How to avoid negative transfer? Given a target domain/task, how to find source domains/tasks to ensure positive transfer.  Transfer learning meets active learning  Given a specific application, which kind of transfer learning methods should be used.
  • 57. Reference  [Thorndike and Woodworth, The Influence of Improvement in one mental function upon the efficiency of the other functions, 1901]  [Taylor and Stone, Transfer Learning for Reinforcement Learning Domains: A Survey, JMLR 2009]  [Pan and Yang, A Survey on Transfer Learning, IEEE TKDE 2009]  [Quionero-Candela, etal, Data Shift in Machine Learning, MIT Press 2009]  [Biltzer etal.. Domain Adaptation with Structural Correspondence Learning, EMNLP 2006]  [Pan etal., Cross-Domain Sentiment Classification via Spectral Feature Alignment, WWW 2010]  [Pan etal., Transfer Learning via Dimensionality Reduction, AAAI 2008]
  • 58. Reference (cont.)  [Pan etal., Domain Adaptation via Transfer Component Analysis, IJCAI 2009]  [Evgeniou and Pontil, Regularized Multi-Task Learning, KDD 2004]  [Zhang and Yeung, A Convex Formulation for Learning Task Relationships in Multi-Task Learning, UAI 2010]  [Saha etal, Learning Multiple Tasks using Manifold Regularization, NIPS 2010]  [Argyriou etal., Multi-Task Feature Learning, NIPS 2007]  [Ando and Zhang, A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data, JMLR 2005]  [Ji etal, Extracting Shared Subspace for Multi-label Classification, KDD 2008]
  • 59. Reference (cont.)  [Raina etal., Self-taught Learning: Transfer Learning from Unlabeled Data, ICML 2007]  [Dai etal., Boosting for Transfer Learning, ICML 2007]  [Glorot etal., Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach, ICML 2011]