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Aug, 2023
无监督领域适应中的领域自适应扩散
Unsupervised Domain Adaptation via Domain-Adaptive Diffusion
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Duo Peng, Qiuhong Ke, Yinjie Lei, Jun Liu
TL;DR
提出一种新颖的领域自适应扩散(DAD)模块和互学习策略(MLS),通过将源域数据逐渐转化为目标域数据并使分类模型在领域转换过程中学习,成功将领域适应的挑战分解为多个小领域间隙并逐步增强分类模型的能力,从而在三个广泛使用的无监督领域自适应数据集上大幅优于现有最先进方法。
Abstract
unsupervised domain adaptation
(UDA) is quite challenging due to the large distribution discrepancy between the source domain and the target domain. Inspired by
diffusion models
which have strong capability to gr
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