基于遷移權(quán)重的條件對(duì)抗領(lǐng)域適應(yīng)
doi: 10.11999/JEIT190115
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重慶郵電大學(xué)數(shù)據(jù)工程與可視計(jì)算重點(diǎn)實(shí)驗(yàn)室 ??重慶 ??400065
Transfer Weight Based Conditional Adversarial Domain Adaptation
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Key Laboratory of Data Engineering and Visual Computing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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摘要: 針對(duì)條件對(duì)抗領(lǐng)域適應(yīng)(CDAN)方法未能充分挖掘樣本的可遷移性,仍然存在部分難以遷移的源域樣本擾亂目標(biāo)域數(shù)據(jù)分布的問(wèn)題,該文提出一種基于遷移權(quán)重的條件對(duì)抗領(lǐng)域適應(yīng)(TW-CDAN)方法。首先利用領(lǐng)域判別模型的判別結(jié)果作為衡量樣本遷移性能的主要度量指標(biāo),使不同的樣本具有不同的遷移性能;其次將樣本的可遷移性作為權(quán)重應(yīng)用在分類(lèi)損失和最小熵?fù)p失上,旨在消除條件對(duì)抗領(lǐng)域適應(yīng)中難以遷移樣本對(duì)模型造成的影響;最后使用Office-31數(shù)據(jù)集的6個(gè)遷移任務(wù)和Office-Home數(shù)據(jù)集的12個(gè)遷移任務(wù)進(jìn)行了實(shí)驗(yàn),該方法在14個(gè)遷移任務(wù)上取得了提升,在平均精度上分別提升1.4%和3.1%。
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關(guān)鍵詞:
- 遷移學(xué)習(xí) /
- 領(lǐng)域適應(yīng) /
- 對(duì)抗學(xué)習(xí) /
- 遷移權(quán)重
Abstract: Considering the failure of the Conditional adversarial Domain AdaptatioN(CDAN) to fully utilize the sample transferability, which still struggle with some hard-to-transfer source samples disturbed the distribution of the target domain samples, a Transfer Weight based Conditional adversarial Domain AdaptatioN(TW-CDAN) is proposed. Firstly, the discriminant results in the domain discriminant model as the main factor are employed to measure the transfer performance. Then the weight is applied to class loss and minimum entropy loss. It is for eliminating the influence of hard-to-transfer samples of the model. Finally, experiments are carried out using the six domain adaptation tasks of the Office-31 dataset and the 12 domain adaptation tasks of the Office-Home dataset. The proposed method improves the 14 domain adaptation tasks and increases the average accuracy by 1.4% and 3.1% respectively.-
Key words:
- Transfer learning /
- Domain adaptation /
- Adversarial learning /
- Transfer Weight(TW)
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表 1 Office-31數(shù)據(jù)集結(jié)果(使用平均精度進(jìn)行評(píng)價(jià))
方法 A→W D→W W→D A→D D→A W→A 平均 ResNet-50[17] 68.4 96.7 99.3 68.9 62.5 60.7 76.1 DAN[7] 80.5 97.1 99.6 78.6 63.6 62.8 80.4 RTN[8] 84.5 96.8 99.4 77.5 66.2 64.8 81.6 DANN[10] 82.0 96.9 99.1 79.7 68.2 67.4 82.2 ADDA[11] 86.2 96.2 98.4 77.8 69.5 68.9 82.9 JAN[18] 85.4 97.4 99.8 84.7 68.6 70.0 84.3 GTA[19] 89.5 97.9 99.8 87.7 72.8 71.4 86.5 CDAN[13] 93.1 98.6 100.0 92.9 71.0 69.3 87.5 TW-CDAN 94.9 99.2 100.0 94.0 72.7 72.5 88.9 下載: 導(dǎo)出CSV
表 2 Office-Home數(shù)據(jù)集結(jié)果(使用平均精度進(jìn)行評(píng)價(jià))
方法 Ar→Cl Ar→Pr Ar→Rw Cl→Ar Cl→Pr Cl→Rw Pr→Ar Pr→Cl Pr→Rw Rw→Ar Rw→Cl Rw→Pr 平均 ResNet-50[17] 34.9 50.0 58.0 37.4 41.9 46.2 38.5 31.2 60.4 53.9 41.2 59.9 46.1 DAN[7] 43.6 57.0 67.9 45.8 56.5 60.4 44.0 43.6 67.7 63.1 51.5 74.3 56.3 DANN[10] 45.6 59.3 70.1 47.0 58.5 60.9 46.1 43.7 68.5 63.2 51.8 76.8 57.6 JAN[18] 45.9 61.2 68.9 50.4 59.7 61.0 45.8 43.4 70.3 63.9 52.4 76.8 58.3 CDAN[13] 50.6 65.9 73.4 55.7 62.7 64.2 51.8 49.1 74.5 68.2 56.9 80.7 62.8 TW-CDAN 48.8 71.1 76.7 61.6 68.9 70.2 60.4 46.6 77.9 71.3 55.4 81.9 65.9 下載: 導(dǎo)出CSV
表 3 不同遷移權(quán)重設(shè)置在Office-31數(shù)據(jù)集結(jié)果(使用平均精度進(jìn)行評(píng)價(jià))
方法 A→W D→W W→D A→D D→A W→A 平均 CDAN[13] 93.1 98.6 100.0 92.9 71.0 69.3 87.5 CDAN(S) 93.0 98.7 100.0 92.7 71.0 69.1 87.4 TW-CDAN(E) 93.7 98.8 100.0 93.4 71.5 71.3 88.1 TW-CDAN(C) 94.2 98.9 100.0 93.1 72.1 71.8 88.4 TW-CDAN 94.9 99.2 100.0 94.0 72.7 72.5 88.9 下載: 導(dǎo)出CSV
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