Transfer Learning with Dynamic Distribution Adaptation

17 Sep 2019Jindong WangYiqiang ChenWenjie FengHan YuMeiyu HuangQiang Yang

Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions... (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 DDA Accuracy 88.9 # 5
Domain Adaptation Office-31 MEDA (ResNet50) Average Accuracy 85.7 # 14
Domain Adaptation Office-Home MEDA (ResNet50) Accuracy 67.3 # 6

Methods used in the Paper


METHOD TYPE
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