When does supervised domain adaptation outperform finetuning?
Best insight from top research papers
Supervised domain adaptation outperforms finetuning when there is an acoustic mismatch between the pretraining and target datasets, and when there is a need to bring the clean dataset closer to the target domain through calibrated data augmentations . Additionally, combining fine-tuned features with feature transformation based methods can lead to improved domain adaptation performance . In the case of semi-supervised domain adaptation, where there are labeled source domain data and a small number of labeled target domain data, adapting the target domain data distribution through model transfer is a better solution than domain alignment by mixing annotated data from two domains .
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| Papers (5) | Insight |
|---|---|
07 Nov 2022 | The provided paper does not mention when supervised domain adaptation outperforms finetuning. |
1 Citations | Supervised domain adaptation is not discussed in the paper. |
25 Oct 2022 | The provided paper does not mention anything about supervised domain adaptation outperforming fine-tuning. |
01 Jun 2023 | Supervised domain adaptation outperforms fine-tuning when there is an acoustic domain mismatch between the pretraining and target datasets. |
| The provided paper does not explicitly mention when supervised domain adaptation outperforms fine-tuning. |
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