Machine Learning

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 isbuilt, there are reasons to believe the model will cease tobe valid at some point in time.

The difference is that now source and target domains canbe completely different.

Motivation Transfer Learning

Traditional Approach to Classification

DB1

DB2

DBn

LearningSystem

Transfer Learning

DB1

DB2

DB new

LearningSystem

Knowledge

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 Data is outdated.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 but

a new survey is now available.

Survey 1

LearningSystem

Survey 2

$C:\CentralRepositoryN\MachineLearning\Publications\Books\MetaLearningBook2007\General\FinalFiles\Melearning\Figs\Transfer.png$

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

LeftLeft

StraightStraight

RightRight

Functional Transfer: Multitask Learning

Given example X, compute the output of every node untilGiven 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 sigmoidCompute sigmoid

function function

Functional Transfer in Neural Networks

$C:\CentralRepositoryN\MachineLearning\Publications\Books\MetaLearningBook2007\General\FinalFiles\Melearning\Figs\example-astronomy.png$

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 prior

distribution.

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 SVMs

initializing 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

$C:\CentralRepository\MachineLearning\Projects\PatternsInAstronomy-Physics\SegmentsMarsLong\fig5tgars.jpg$

Identify common

Features 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

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:

LearningSystem

Target

domain

Source

domain

Filter samples

Larger target dataset

New program called

TrAdaboost

Instance Transfer Learning

New program called

TrAdaboost

•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

Incorrectly classified

instances  increase weight

Boosting

Combine all hypotheses to produce final weighted function:

w1 f1 + w2 f2 + … + wn fn

Automatic Instance Transfer

Boosting

Source

domain

Target

domain

LearningSystem

(Boosting)

Incorrectly classified

instances  decrease weight

Incorrectly classified

instances  increase weight