1) The document presents a new deep unsupervised domain adaptation method that uses graph matching and pseudo-label guided training. 2) It introduces a second-order matching term to capture structural correspondence between domains, in addition to a first-order term. 3) The method performs a two-stage training, where in the first stage it reduces domain discrepancy using graph matching, and in the second stage it exploits unlabeled target data with pseudo-labels to further refine the decision boundaries.

1. 1
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Graph Matching and Pseudo-Label Guided Deep
Unsupervised Domain Adaptation
Debasmit Das C.S. George Lee
Assistive Robotics Technology Laboratory
School of Electrical and Computer Engineering
Purdue University, West Lafayette, IN, USA
Funding Source : National Science Foundation (IIS-1813935)

2. 2
Outline
• INTRODUCTION
• OUR METHOD
• EXPERIMENTAL RESULTS

3. 3
Introduction
Classical Machine Learning Setting
Find where
Training and Testing
Samples from same
Distribution !!

4. 4
Introduction
Classifying a Dog and a Cat
Training Samples Testing Samples
Training and Testing
Distribution different!!
Domain Adaptation
Required!!

5. 5
Introduction
Domain Adaptation Methods
Non-Deep Methods Deep Methods
• Instance Re-weighting
[Dai et al. ICML’07]
• Parameter Adaptation
[Bruzzone et al. TPAMI’10]
• Feature Transformation
[Fernando et al. ICCV’13]
[Sun et al. AAAI’16]
• Discrepancy Based
[Long et al. ICML’15]
[Sun et al. ECCV’16]
• Adversarial Based
[Ganin et al. JMLR’16]
[Tzeng et al. CVPR’17]

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Introduction
• Discrepancy Based Methods
Mostly global metrics. Minimizes statistics of data like covariance
[Sun et al. ECCV’16] or maximum mean discrepancy [Long et al. ICML’15]
• Local Method
Optimal Transport (Courty et al. TPAMI’17). Basically point-point Matching
Using first order information might be misleading. How ?

7. 7
Second order information
Our Method

8. 8
Representing Correspondences
Correspondence Matrix
1
0
Matching Matrix
Continuous
Relaxation
Our Method

9. 9
First-order Matching
Continuous
Relaxation
Our Method
Continuous
Relaxation
Second-order Matching

10. 10
Our Method
Optimization
With additional correction
Add equality constraints as
penalty

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Our Method
Optimization
Classification Loss
Total Loss

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Closed form solution of mapping
Iterative solution
Our Method

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Second stage training procedure
• Domain Discrepancy is
reduced
• Need to exploit unlabeled data
• Chose confident unlabeled
samples based on a threshold
• ‘Sharpen’ the probability of
these confident samples
Our Method

14. 14
Pseudo-label (PL) Loss
Our Method
Class 2Class 1
Class 3
Sample 1 Sample 2
Sample 3

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Second stage training procedure
Total Loss
Our Method

16. 16
Overall architecture
Stage 1 training Stage 2 training
Our Method

17. 17
Experimental Results
Dataset Settings
tunable
Batch Size – 32 source & target samples
Input – Decaf features [ICML‘14]
Feature Extractor – [500,100] FC ReLU
Optimizer – Adam [ICLR’15]
Implementation – Tensorflow on
NVIDIA GeForce GPUDataset available at https://github.com/RockySJ/WDGRL

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Experimental Results
Comparison with previous work
MMD [JMLR’12], DANN [JMLR’16], CORAL [ECCV’16], WDGRL [AAAI’18]
(Maximum Mean Discrepancy) (Domain Adversarial) (Correlation alignment) (Wasserstein Distance)

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Experimental Results
Parameter Sensitivity
Second-order term over First-order term Graph-Matching Loss over Classification loss Classification loss over Pseudo-label loss

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Experimental Results
Convergence

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Experimental Results
Feature Visualization
Without Adaptation With Graph Matching With Graph Matching
& Pseudo-labeling

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• Second order matching term is
important to match structure in data
• Refining decision boundaries by
self-training with unlabeled data is
beneficial
• Performance improvement on image
recognition justifies the inclusion of
the 2-stage training
Conclusion
Future Work
Multiple source domain generalization

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THANK YOU
Any Questions ?