论文笔记：Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
Profile 最近因为个人需要看了一些 D A DA DA 和 D R DR DR 的文章。 这篇文章比较有意思的是：将聚类设计成目标函数，从而一方面实现经网络抽取特征在分布上的自然聚类，一方面因为特征分布的聚类自然提高了分类效果，一方面加快了聚类的速度，同时最主要的就是实现了不同源域之间的结构的细粒度对齐，从而促进 U D A UDA UDA 算法的性能。 In
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相关文章
- 1. 《A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification》论文笔记
- 2. 论文笔记：Unsupervised Domain Adaptation by Backpropagation
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- 4. A DIRT-T APPROACH TO UNSUPERVISED DOMAIN ADAPTATION
- 5. 《Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision》论文笔记
- 6. [论文阅读笔记@2019.6] Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 7. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 8. 论文笔记：Domain Adaptation for Image Dehazing
- 9. 论文笔记 —— 《Asymmetric Tri-training for Unsupervised Domain Adaptation》
- 10. 【论文笔记】Asymmetric Tri-training for Unsupervised Domain Adaptation