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- W4292388283 abstract "Domain adaption has been found to suffer from a potentially severe degradation in migration performance when facing source and target domains with insufficient shared representations. Active learning is introduced to avoid the problem by providing partial target domain data labels. However, the addition of active learning inevitably brings the problem of high manual labeling costs. This paper proposes an active generation domain adaption method (AGDA). Only the first round of data labeling requires manual participation. The target domain classifier will undertake the labeling task from the second round. The labeled data will be selected and used to generate a large amount of labeled fake target domain data to train the target domain classifier. The fewer manual labeling is made possible by the HcTop data selection method proposed in this paper, which selects the batch of samples predicted by the classifier with the highest confidence. The HcTop method can select the “confusing” data valuable for migration. Experiments demonstrate that the AGDA method can achieve higher migration performance with less manual labeling in the case of insufficient shared representations." @default.
- W4292388283 created "2022-08-20" @default.
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- W4292388283 date "2022-03-01" @default.
- W4292388283 modified "2023-10-18" @default.
- W4292388283 title "High-performance Active Domain Adaption with Fewer Manual Labeling" @default.
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- W4292388283 doi "https://doi.org/10.1109/cacml55074.2022.00080" @default.
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