基于特征校正的多对抗域适应方法

doi:10.11959/j.issn.1000-0801.2024014

Abstract

Abstract:

Domain adaptation can transfer labeled source domain information to an unlabeled but related target domain by aligning the distribution of source domain and target domain.However, most existing methods only align the low-level feature distributions of the source and target domains, failing to capture fine-grained information within the samples.To address this limitation, a feature correction-based multi-adversarial domain adaptation method was proposed.An attention mechanism to highlight transferable regions was introduced in this method and a feature correction module was deployed to align the high-level feature distributions between the two domains, further reducing domain discrepancies.Additionally, to prevent individual classifiers from overfitting their own noisy pseudo-labels,dual classifier co-training was proposed and the feature aggregation property of graph neural networks was utilized to generate more accurate source domain labels.Extensive experiments on three benchmark datasets for transfer learning demonstrate the effectiveness of the proposed method.

Key words: domain adaptation, transfer learning, adversarial network, attention mechanism

CLC Number:

TP181

Yong ZHANG, Haoshuang LIU, Qi ZHANG, Wenzhe LIU. Multi-adversarial domain adaptation method based on feature correction[J]. Telecommunications Science, 2024, 40(1): 71-82.

Figures/Tables 7

References 32

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数据集	子域	样本数/个	类别数
Office-31	Amazon (A)	2 817	31
	DSLR (D)	498	31
	Webcam (W)	795	31
ImageCLEF-DA	Caltech (C)	600	12
	ImageNet (I)	600	12
	Pascal (P)	600	12
Office-Home	Artistic (Ar)	2 427	65
	Clip Art (Cl)	4 365	65
	Product (Pr)	4 439	65
	Real-World (Rw)	4 357	65

方法	A→W	D→W	W→D	A→D	D→A	W→A	AVG
ResNet-50	68.4%	96.7%	99.3%	68.9%	62.5%	60.7%	76.1%
DAN	83.8%	96.8%	99.5%	78.4%	66.7%	62.7%	80.4%
D-CORAL	77.7%	97.6%	99.7%	81.1%	64.6%	64%	80.8%
DANN	82%	96.9%	99.1%	79.7%	68.2%	67.4%	82.2%
JAN	85.4%	97.4%	99.8%	84.7%	68.6%	70%	84.3%
MADA	90%	97.4%	99.6%	87.8%	70.3%	66.4%	85.2%
CDAN	93.1%	98.2%	100%	89.8%	70.1%	68%	86.6%
TADA	94.3%	98.7%	99.8%	91.6%	72.9%	73%	88.4%
DSAN	93.6%	98.3%	100%	90.2%	73.5%	74.8%	88.4%
ETD	92.1%	100%	100%	88%	71%	67.8%	86.2%
CaCo	89.7%	98.4%	100%	91.7%	73.1%	72.8%	87.6%
本文算法	94%	98.7%	100%	92.4%	73.8%	73.6%	88.8%

方法	I→P	P→I	I→C	C→I	C→P	P→C	AVG
ResNet-50	74.8%	83.9%	91.5%	78.0%	65.5%	91.2%	80.8%
DAN	74.5%	82.2%	92.8%	86.3%	69.2%	89.8%	82.5%
DANN	75.0%	86.0%	96.2%	87.0%	74.3%	91.5%	85.0%
MADA	75.0%	87.9%	96.0%	88.8%	75.2%	92.2%	85.9%
DCAN	80.5%	91.2%	95.7%	91.8%	77.2%	93.3%	88.3%
本文算法	78.7%	91.9%	96.5%	92.3%	77.8%	95.0%	88.7%

方法方法	A→C	A→P	A→R	C→A	C→P	C→R	P→A	P→C	P→R	R→A	R→C	R→P	AVG
ResNet-50	34.9%	50.0%	58.0%	37.4%	41.9%	46.2%	38.5%	31.2%	60.4%	63.9%	41.2%	59.9%	47.0%
DAN	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	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	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	50.7%	70.6%	76.0%	57.6%	70.0%	70.0%	57.4%	50.9%	77.3%	70.9%	56.7%	81.6%	65.8%
TADA	53.1%	72.3%	77.2%	59.1%	71.2%	72.1%	59.7%	53.1%	78.7%	72.4%	60.0%	82.9%	67.7%
DSAN	54.4%	70.8%	75.4%	60.4%	67.8%	68.0%	62.6%	55.9%	78.5%	73.8%	60.6%	83.1%	67.6%
ALDA	53.7%	70.1%	76.4%	60.2%	72.6%	71.5%	56.8%	51.9%	77.1%	70.2%	56.3%	82.1%	66.6%
本文算法	55.6%	71.3%	78.0%	61.8%	71.8%	73.5%	61.5%	54.5%	80.0%	72.8%	60.9%	83.2%	68.7%

Multi-adversarial domain adaptation method based on feature correction

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