008 20151221 return of frustrating easy domain adaptation
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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
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
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