Return of Frustrating Easy Domain Adaptation

Tran Quoc Hoan

@k09ht haduonght.wordpress.com/

21 December 2015, Paper Alert, Hasegawa lab., Tokyo

The University of Tokyo

Baochen Sun, Jiashi Feng, Kate Saenko, AAAI-16

Abstract

Return of Frustratingly Easy Domain Adaptation 2

“…We propose a simple, effective, and efficient method for

unsupervised domain adaptation called CORelation

Alignment (CORAL). CORAL minimizes domain shift by

aligning the second-order statistics of source and target

distributions, without requiring any target labels…”

“Everything should be made as simple as possible, but not simpler”

- Albert Einstein-

Outline

3

- Domain Shift Scenarios

• Motivation

- Correlation Alignment for Unsupervised Domain Adaptation

• Proposal

- Object Recognition

• Experiments

- Sentiment Analysis

Return of Frustratingly Easy Domain Adaptation

Domain Shift Scenariors

4Return of Frustratingly Easy Domain Adaptation

Source

Target

(often unlabeled, requiring unsupervised adaptation)

When data distributions differ across domains, applying classifiers trained on one domain directly to another domain is likely to cause a significant performance drop

Correlation Alignment

5Return of Frustratingly Easy Domain Adaptation

Original Source de-correlation

Target re-correlation

In case of whitening both source and target but fail (different subspaces)Training on this

adapted domain

Formulation

6Return of Frustratingly Easy Domain Adaptation

Derivation

7Return of Frustratingly Easy Domain Adaptation

Whitening Re-colors with target covariance

Algorithm

8Return of Frustratingly Easy Domain Adaptation

Whitening with regulation

- Consider each layer (i.e. fc) as features vector

Apply for DNN

Experiments

9Return of Frustratingly Easy Domain Adaptation

Experiments

10Return of Frustratingly Easy Domain Adaptation