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

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-ENT Accuracy 89.1 # 4
Domain Adaptation ImageCLEF-DA DFA-SAFN Accuracy 90.2 # 2
Domain Adaptation MNIST-to-USPS DFA-ENT Accuracy 97.9 # 4
Domain Adaptation MNIST-to-USPS DFA-MCD Accuracy 98.6 # 1
Transfer Learning Office-Home DFA-SAFN Accuracy 69.1 # 2
Transfer Learning Office-Home DFA-ENT Accuracy 69.2 # 1
Domain Adaptation SVHN-to-MNIST DFA-MCD Accuracy 98.9 # 2
Domain Adaptation SVHN-to-MNIST DFA-ENT Accuracy 98.2 # 3
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 # 7
Domain Adaptation USPS-to-MNIST DFA-MCD Accuracy 96.6 # 5

Methods used in the Paper


METHOD TYPE
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