Divergence Optimization for Noisy UniDA

Qing Yu, Atsushi Hashimoto and Yoshitaka Ushiku, "Divergence Optimization for Noisy Universal Domain Adaptation", CVPR'21
paper: https://arxiv.org/abs/2104.00246

Transcript

1. Qing Yu1,2, Atsushi Hashimoto2, Yoshitaka Ushiku2 (1The University of Tokyo, 2OMRON SINIC X) Divergence Optimization for Noisy Universal Domain Adaptation
2. Unsupervised Domain Adaptation (UDA) 2
3. Partial UDA 3
4. Open-set UDA 4
5. Universal UDA (UniUDA) 5
6. Our Setting for Noisy UniUDA 6
7. Related Works 7 Method Noisy labels Partial UDA Open-set UDA DANN [Ganin+, ICML 15] ✖ ✖ ✖ TCL [Shu+, AAAI 19] ✔ ✖ ✖ ETN [Cao+, CVPR 19] ✖ ✔ ✖ STA [Liu+, CVPR 19] ✖ ✖ ✔ UAN [You+, CVPR 19] ✖ ✔ ✔ DANCE [Saito+, NeurIPS 20] ✖ ✔ ✔ Ours ✔ ✔ ✔
8. Overall Concept 8 CNN-1 Noisy dataset CNN-2 Different parameters distinct views of each sample
9. Overall Concept 9 CNN-1 CNN-2 Different parameters Dog Divergence Dog Cat 1.0 0.0 Dog Cat 1.0 0.0 Entropy Entropy Cross Entropy
10. Overall Concept 10 CNN-1 CNN-2 Different parameters Dog Divergence Dog Cat 0.8 0.2 Dog Cat 0.5 0.5 Entropy Entropy Cross Entropy
11. Our Setting for Noisy UniUDA 11 Noisy data Also Noisy data
12. • Step-1: Train on noisy labeled source samples Proposed Method 12 G F1 F2 Labeled source samples Divergence Probabilities Supervised Loss Supervised Loss Total Loss
13. Proposed Method 13 G F1 F2 Labeled source samples Probabilities Supervised Loss Supervised Loss Total Loss • Step-1: Train on noisy labeled source samples Samples having small loss ↓ Clean source samples Divergence
14. Proposed Method 14 • Step-1: Train on noisy labeled source samples -> Train the classifiers under the supervision without detected noisy labels Clean source samples Classifiers for ▲ Target samples Noisy source samples
15. • Step-2: Train on unlabeled target samples Proposed Method 15 G F1 F2 Unlabeled target samples Probabilities Divergence Separation Large divergence -> Target private samples Small divergence -> Common samples
16. • Step-2: Train on unlabeled target samples -> Align the distribution of target common/private samples according to the divergence Proposed Method 16 Align target common samples into small divergence area Align targe private samples into large divergence area
17. • Step-3: Train on unlabeled target samples Proposed Method 17 G F1 F2 Unlabeled target samples Probabilities Maximize Divergence
18. • Step-3: Train on unlabeled target samples Proposed Method 18 Updated classifiers by maximizing divergence to detect target samples Source samples are also used to keep classifiers
19. • Step-4: Train on unlabeled target samples Proposed Method 19 G F1 F2 Unlabeled target samples Probabilities Minimize Divergence common samples having small divergence
20. • Step-4: Train on unlabeled target samples Proposed Method 20 Align targe common samples into the cluster of source samples
21. Inference 21 Test target samples Divergence > Threshold? Large divergence -> Target private samples Small divergence -> Common samples -> Class prediction G F2 Probabilities F1
22. Experiments on Toy Data 22 Source samples Target samples Classifier-1 for Classifier-2 for Area of Area of Area of Area having large divergence
23. Experiments on Benchmarks • Dataset (Source private/Common/Target private): • Office (10/10/11) • Office-Home (5/10/50) • Visda (3/6/3) • Training source data: • Label noise: pairflip (P), symmetry (S) • Nosie Level: 20%, 45% -> P20, P45, S20, S45 23 pairflip noise transition matrix True label Corrupted label
24. Experiments on Benchmarks • Dataset (Source private/Common/Target private): • Office (10/10/11) • Office-Home (5/10/50) • Visda (3/6/3) • Training source data: • Label noise: pairflip (P), symmetry (S) • Nosie Level: 20%, 45% -> P20, P45, S20, S45 24 True label Corrupted label symmetric noise transition matrix
25. Results • Test Accuracy (Common classes + target private class) ->State-of-the-art performance in most tasks 25
26. Results • Probability density function of the divergence of common and target private samples ->The divergence of common and target private samples is separated 26 0.0 0.2 0 0.4 Density 0.6 0.8 1 2 3 4 5 6 Divergence
27. Conclusion • We proposed divergence optimization for Noisy UniUDA. • We used two classifiers to find clean source samples, reject target private classes, and find important target samples that contribute most to the model’s adaptation. • Our method achieved high performance on a diverse set of benchmarks. 27