Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment

23 Jun 2020Jing WangJiahong ChenJianzhe LinLeonid SigalClarence W. de Silva

In this study, we focus on the unsupervised domain adaptation problem where an approximate inference model is to be learned from a labeled data domain and expected to generalize well to an unlabeled data domain. The success of unsupervised domain adaptation largely relies on the cross-domain feature alignment... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Domain Adaptation ImageCLEF-DA DFA-SAFN Accuracy 90.2 # 2
Domain Adaptation ImageCLEF-DA DFA-ENT Accuracy 89.1 # 4
Domain Adaptation MNIST-to-USPS DFA-MCD Accuracy 98.6 # 1
Domain Adaptation MNIST-to-USPS DFA-ENT Accuracy 97.9 # 3
Transfer Learning Office-Home DFA-ENT Accuracy 69.2 # 1
Transfer Learning Office-Home DFA-SAFN Accuracy 69.1 # 2
Domain Adaptation SVHN-to-MNIST DFA-MCD Accuracy 98.9 # 1
Domain Adaptation SVHN-to-MNIST DFA-ENT Accuracy 98.2 # 2
Domain Adaptation SYNSIG-to-GTSRB DFA-MCD Accuracy 97.5 # 1
Domain Adaptation SYNSIG-to-GTSRB DFA-ENT Accuracy 96.8 # 2
Domain Adaptation USPS-to-MNIST DFA-ENT Accuracy 96.2 # 6
Domain Adaptation USPS-to-MNIST DFA-MCD Accuracy 96.6 # 4

Methods used in the Paper


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