Transfer Learning Motivation and Types Functional Transfer Learning Representational Transfer...
-
Upload
luke-moody -
Category
Documents
-
view
234 -
download
0
Transcript of Transfer Learning Motivation and Types Functional Transfer Learning Representational Transfer...
Transfer Learning
• Motivation and Types
• Functional Transfer Learning• Representational Transfer Learning• References
• The goal is to transfer knowledge gathered from previous experience.
• Also called Inductive Transfer or Learning to Learn.
• Example: Invariant transformations across tasks.
Transfer Learning
Motivation for transfer learning
Similar to self-adaptation: once a predictive model is built, there are reasons to believe the model will cease to be valid at some point in time.
The difference is that now source and target domains can be completely different.
Motivation Transfer Learning
Traditional Approach to Classification
DB1 DB2 DBn
Learning System
Learning System
Learning System
Transfer Learning
DB1 DB2
DB new
Learning System
Learning System
Learning SystemKnowledge
Source domain
Target domain
Transfer Learning
Scenarios:
1.Labeling in a new domain is costly.
DB1 (labeled)
Classification of Cepheids
DB2 (unlabeled)
Classification of LPV
Transfer Learning
Scenarios:
2. Data is outdated. Model created with one survey buta new survey is now available.
Survey 1
Learning System
Survey 2
?
Types of Transfer Learning
Figure obtained from Brazdil, et. Al. Metalearning: Applications to Data Mining, Chapter 7, Springer, 2009.
Transfer Learning
• Motivation and Types
• Functional Transfer Learning• Representational Transfer Learning• References
Input nodesInput nodes
Internal nodesInternal nodes
Output nodesOutput nodes
Left Left StraightStraight RightRight
Functional Transfer: Multitask Learning
Given example X, compute the output of every node until Given example X, compute the output of every node until we reach the output nodes:we reach the output nodes:
Input nodesInput nodes
Internal nodesInternal nodes
Output nodesOutput nodes
Example XExample X
Compute sigmoid Compute sigmoid functionfunction
Functional Transfer in Neural Networks
Train in Parallel with Combined Architecture
Figure obtained from Brazdil, et. Al. Metalearning: Applications to Data Mining, Chapter 7, Springer, 2009.
Transfer Learning
• Motivation and Types
• Functional Transfer Learning• Representational Transfer Learning• References
Knowledge of Parameters
Assume prior distribution of parameters
Source domain
Learn parameters and adjust prior distribution
Target domain
Learn parameters using the source priordistribution.
P(y|x) = P(x|y) P(y) / P(x)
Parameter Similarity
Task A Parameter A
Task B Parameter B ~ A
Assume hyper-distribution with low variance.
Assume Parameter Similarity
Knowledge of Parameters
Find coefficients ws using SVMs
Find coefficients wT using SVMsinitializing the search with ws
Feature Transfer
Feature Transfer:
Target domain
Source domain
Shared representation across tasks
Minimize Loss-Function( y, f(x))
The minimization is done over multiple tasks (multiple regions on Mars).
Feature Transfer
Identify commonFeatures to all tasks
Coded divided into pieces
New Solution
Add pieces of code from previous tasks
Start a new solution from scratch
Meta-Searching for Problem Solvers
Exploitation: Maximize reward
vs
Exploration: Maximize long-term success.
Learn to keep the ball away from the opponent.
First Task
Learn to score the opponent.
Second Task
Transfer Learning in Robotics
Instance Transfer Learning
Instance Transfer:
Learning System
Target domainSource
domainFilter samples
Larger target dataset
New program calledTrAdaboost
Instance Transfer Learning
New program calledTrAdaboost
•The main idea is to have a methodology to deal with a changing distribution.
•Examples in the source domain that look as belonging to a diff. distribution are discarded.
•Examples in the source domain that look similar to the target domain are added to the training set.
Boosting
DB
Incorrectly classifiedinstances increase weight
11
1
1 11
1
111
111
11
2
2 2
22
Boosting
DB
11
11
1
2
2
22
22
11
Combine all hypotheses to produce final weighted function:
w1 f1 + w2 f2 + … + wn fn
Automatic Instance Transfer
Boosting
Source domain
Target domain
Learning System
(Boosting)
Incorrectly classifiedinstances decrease weight
Incorrectly classifiedinstances increase weight
Boosting for Transfer Learning, Wenyuan Dai, et. al. ICML 2007
Automatic Instance Transfer
Boosting for Transfer Learning, Wenyuan Dai, et. al. ICML 2007
Automatic Instance Transfer
Boosting for Transfer Learning, Wenyuan Dai, et. al. ICML 2007
Automatic Instance Transfer
Boosting for Transfer Learning, Wenyuan Dai, et. al. ICML 2007
Automatic Instance Transfer
Boosting for Transfer Learning, Wenyuan Dai, et. al. ICML 2007
Automatic Instance Transfer
Boosting for Transfer Learning, Wenyuan Dai, et. al. ICML 2007
Automatic Instance Transfer
Boosting for Transfer Learning, Wenyuan Dai, et. al. ICML 2007
Transfer Learning
• Motivation and Types
• Functional Transfer Learning• Representational Transfer Learning• References
Sinno Jialin Pan and Qiang Yang. A Survey on Transfer Learning.IEEE Transactions on Knowledge and Data Engineering, 22(10):1345-1359, Oct. 2010
Brazdil, P. et. al. Metalearning: Applications to Data Mining. Springer, 2009.
Dai, W., Boosting for Transfer Learning, Proceedings of ICML 2007.
Video on transfer learninghttp://www.youtube.com/watch?v=9ChVn3xVNDI&noredirect=1
References
Sinno Jialin Pan and Qiang Yang. A Survey on Transfer Learning.IEEE Transactions on Knowledge and Data Engineering, 22(10):1345-1359, Oct. 2010
Brazdil, P. et. al. Metalearning: Applications to Data Mining. Springer, 2009.
Dai, W., Boosting for Transfer Learning, Proceedings of ICML 2007.
Video on transfer learninghttp://www.youtube.com/watch?v=9ChVn3xVNDI&noredirect=1
References
Robot learns to flip pancakes
http://www.youtube.com/watch?v=W_gxLKSsSIE&noredirect=1
Robot learns to stack pancakes
http://www.youtube.com/watch?v=v9oeOYMRvuQ
Videos