Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation

We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution shift---from a class-conditioned domain alignment perspective. Current methods for class-conditioned domain alignment aim to explicitly minimize a loss function based on pseudo-label estimations of the target domain... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Unsupervised Domain Adaptation Office-31 Implicit Alignment (with MDD) Avg accuracy 88.8 # 1
Unsupervised Domain Adaptation Office-Home Implicit Alignment (with MDD) Avg accuracy 69.5 # 1
Unsupervised Domain Adaptation Office-Home (RS-UT imbalance) DANN Average Per-Class Accuracy 56.91 # 3
Unsupervised Domain Adaptation Office-Home (RS-UT imbalance) MDD Average Per-Class Accuracy 55.44 # 4
Unsupervised Domain Adaptation Office-Home (RS-UT imbalance) COAL Average Per-Class Accuracy 58.4 # 2
Unsupervised Domain Adaptation Office-Home (RS-UT imbalance) Implicit Alignment (with MDD) Average Per-Class Accuracy 61.67 # 1
Unsupervised Domain Adaptation Office-Home (RS-UT imbalance) Source Only Average Per-Class Accuracy 52.81 # 5
Unsupervised Domain Adaptation VisDA2017 Implicit Alignment (with MDD) Accuracy 75.8 # 1

Methods used in the Paper


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