On Minimum Discrepancy Estimation for Deep Domain Adaptation

Rahman, Mohammad Mahfujur; Fookes, Clinton; Baktashmotlagh, Mahsa; Sridharan, Sridha

doi:10.48550/arxiv.1901.00282

Cited by 1 publication

(1 citation statement)

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“…The method adds different distance loss functions to the artificial neural network. The most widely used metric schemas include Maximum Mean Discrepancy (MMD) [8][9][10], KL (Kullback-Leibler) divergence [11], JS (Jensen-Shannon) divergence [12], Wasserstein distance [13][14][15], CORAL (CORrelation ALignment) [16,17], etc.…”

Section: Introductionmentioning

confidence: 99%

Research on Adversarial Domain Adaptation Method and Its Application in Power Load Forecasting

Huang

Yin

2022

Mathematics

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Domain adaptation has been used to transfer the knowledge from the source domain to the target domain where training data is insufficient in the target domain; thus, it can overcome the data shortage problem of power load forecasting effectively. Inspired by Generative Adversarial Networks (GANs), adversarial domain adaptation transfers knowledge in adversarial learning. Existing adversarial domain adaptation faces the problems of adversarial disequilibrium and a lack of transferability quantification, which will eventually decrease the prediction accuracy. To address this issue, a novel adversarial domain adaptation method is proposed. Firstly, by analyzing the causes of the adversarial disequilibrium, an initial state fusion strategy is proposed to improve the reliability of the domain discriminator, thus maintaining the adversarial equilibrium. Secondly, domain similarity is calculated to quantify the transferability of source domain samples based on information entropy; through weighting in the process of domain alignment, the knowledge is transferred selectively and the negative transfer is suppressed. Finally, the Building Data Genome Project 2 (BDGP2) dataset is used to validate the proposed method. The experimental results demonstrate that the proposed method can alleviate the problem of adversarial disequilibrium and reasonably quantify the transferability to improve the accuracy of power load forecasting.

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