Transfer Learning for Transfer Learning for Image Image

ClassificationClassification

Group No.: 15Group No.: 15 Group member : Feng Cai Group member : Feng Cai caixx043@umn.educaixx043@umn.edu Sauptik Dhar Sauptik Dhar dharx007@umn.edudharx007@umn.edu Jingying Lin Jingying Lin linxx634@umn.edulinxx634@umn.edu

Group Project for EE5561 09 SpringGroup Project for EE5561 09 Spring

CURRENT STATE OF ART(SELF TAUGHT LEARNING CURRENT STATE OF ART(SELF TAUGHT LEARNING with SPARSE CODING)with SPARSE CODING)

OUR METHODS (OUR METHODS (UNSUPERVISEDUNSUPERVISED TRANSFER TRANSFER LEARNING)LEARNING)

OUR METHODS (OUR METHODS (SUPERVISEDSUPERVISED TRANSFER LEARNING) TRANSFER LEARNING)

EXPERIMENTAL SETUP/DATASETEXPERIMENTAL SETUP/DATASET

RESULTSRESULTS

CONCLUSIONCONCLUSION

BRIEF OUTLINE

SELF-TAUGHT LEARNING WHAT IS SPARSE CODING?WHAT IS SPARSE CODING?

Sparse coding is the representation of items by the strong Sparse coding is the representation of items by the strong activation of a activation of a

relatively small set.relatively small set.BASIC FORMULATIONBASIC FORMULATION

WHAT IS SELF-TAUGHT LEARNING? WHAT IS SELF-TAUGHT LEARNING? [1][1]

Unlike Semi-Supervised classification ;no assumption that Unlike Semi-Supervised classification ;no assumption that unlabeled data follows the same class labels or generative unlabeled data follows the same class labels or generative distribution as the labeled data.distribution as the labeled data.

WHAT IS TRANSFER LEARNING? WHAT IS TRANSFER LEARNING? [2][2]

Involves two interrelated learning problems with the goal of Involves two interrelated learning problems with the goal of using knowledge about one set of task to improve performance using knowledge about one set of task to improve performance on a related task.on a related task.

i

i

i jj

ij

iuab abax 1

)(22

)()(, ||||||||min

[Details: An extra normalization constraint on bj is required.]

UNSUPERVISED TRANSFER LEARNING STEP 1STEP 1: USE SELF LEARNING APPROACH TO OBTAIN THE BASIS : USE SELF LEARNING APPROACH TO OBTAIN THE BASIS

VECTORS.[1]VECTORS.[1]

STEP 2STEP 2: FIND THE COEFFICIENTS C USING FOLLOWING : FIND THE COEFFICIENTS C USING FOLLOWING EQUATIONSEQUATIONS

Define the estimation of as: Define the estimation of as:

Here is a pseudo-norm that counts the number of non-zero Here is a pseudo-norm that counts the number of non-zero rows in .rows in .

The coefficient for example i in group k can be computed as:The coefficient for example i in group k can be computed as:

STEP3STEP3: ARE USED AS NEW FEATURES AND WE TRAIN SVM : ARE USED AS NEW FEATURES AND WE TRAIN SVM CLASSIFIERS IN EACH GROUPCLASSIFIERS IN EACH GROUP

kix

j jkj

kij

ki bwax̂

k

n

ir

ki

kik

aW

k

Wxx1

022

,||||||ˆ||min

0|||| rW WkiC

kiC

kj

kij

kij waC

SUPERVISED TRANSFER LEARNING STEP 1STEP 1: USE SELF LEARNING APPROACH TO OBTAIN THE : USE SELF LEARNING APPROACH TO OBTAIN THE

BASIS VECTORS.[1]BASIS VECTORS.[1]

STEP 2STEP 2: MAP THE LABLED TRAINING DATA IN THE BASIS : MAP THE LABLED TRAINING DATA IN THE BASIS SPACESPACE

STEP 3STEP 3:PERFORM SUPERVISED TRANSFER LEARNING WITH :PERFORM SUPERVISED TRANSFER LEARNING WITH SPARSE CODING.[2] SPARSE CODING.[2]

Let,Let,

STEP 4STEP 4:COMPUTE THE RELEVANT PROTOTYPE :COMPUTE THE RELEVANT PROTOTYPE REPRESENTATIONREPRESENTATION

Finally,Finally,

DATASETDATASETUNLABELED DATASETUNLABELED DATASET( The Yale Face Database B )( The Yale Face Database B )Contains 5760 single light source images of 10 subjects each seen under 576 Contains 5760 single light source images of 10 subjects each seen under 576 viewing viewing

conditions.(http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html)conditions.(http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html)

LABELED DATASETLABELED DATASET(CMU Face Images Data Set )(CMU Face Images Data Set )This data consists of 640 gray level face images of people taken with varying This data consists of 640 gray level face images of people taken with varying

pose and pose and expression.(http://archive.ics.uci.edu/ml/datasets/CMU+Face+Images)expression.(http://archive.ics.uci.edu/ml/datasets/CMU+Face+Images)

EXPERIMENTAL SETUPEXPERIMENTAL SETUPClassification of FACIAL EXPRESSION using TRANSFER LEARNING.Classification of FACIAL EXPRESSION using TRANSFER LEARNING.

CLASS LABELS = Happy(+1) or Sad(-1).GROUP LABELS = PERSON ID.

SCALED DOWN PROBLEM

Number of Unlabeled samples =15Number of Basis used =25Number of Tasks =3

Number of Training samples(Labeled) =56Number of Test samples(Labeled) =19

EXPERIMENTAL SETUP/DATASET

RESULTS

METHOD USED PREDICTION ERROR

RAW DATA (dim=256) 0.421053

SELF-LEARNING (dim=25)(1) 0.421053

SUPERVISED TRANSFER LEARNING (dim=13)

0.421053

METHOD USED PREDICTION ERROR [5 5]

PREDICTION ERROR [10 10]

RAW DATA (dim=256) 0.322727 0.306667

SELF-LEARNING (dim=25)(1) 0.375758 0.346667

SUPERVISED TRANSFER LEARNING (dim=13)

0.322727 0.306667

TRAINING SET=56 samples TEST SET=19 samples

DOUBLE RESAMPLING (56 samples)

TABLE 1. PREDICTION ERROR for LINEAR SVM (for different methods)

TABLE 2. PREDICTION ERROR for LINEAR SVM (for different methods)

(1) There is a caveat involved in obtaining the results for this method.

REFERENCE

[1] Self-taught learning: transfer learning from unlabeled data. [1] Self-taught learning: transfer learning from unlabeled data. Rajat Raina, Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Pacher, Andrew Y. Ng. 24th International Alexis Battle, Honglak Lee, Benjamin Pacher, Andrew Y. Ng. 24th International Conference on Machine Learning 2007.Conference on Machine Learning 2007.

[2] Transfer learning for image classification with sparse prototype [2] Transfer learning for image classification with sparse prototype representations. representations. Ariadna Quattoni, Michael Collins, Trevor Darrell. IEEE CVPR Ariadna Quattoni, Michael Collins, Trevor Darrell. IEEE CVPR 2008.2008.

CONCLUSION

1. The feature selection methodology conserves the discriminative patterns with the added advantage of a lower problem dimensionality.

2. The new transfer learning methodology provides better results than the self-learning approach(at least for the current case).