Unsupervised Domain Adaptation Through Self-Supervision论文阅读笔记
这篇文章是arxiv上的一篇domian adaptation的文章，觉得这里使用的方法和之前的方法完全不同，很有意思。所以整理了一下，和大家一起分享。 本文讨论了无监督域自适应，即源域上有标记的训练数据，但目标是在目标域上只有未标记数据，希望能够通过一些方法将源域上分类器迁移到目标域上，并能在目标域上有良好的性能。与之前的许多工作一样，我们试图在保持可辨别性的同时，对源域和目标域的学习表示进行对
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相关文章
- 1. 笔记：论文阅读Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
- 2. 论文阅读：Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
- 3. [论文阅读笔记@2019.6] Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 4. 《Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision》论文笔记
- 5. 论文笔记：Unsupervised Domain Adaptation by Backpropagation
- 6. 论文笔记 ——《Unsupervised Domain Adaptation by Backpropagation》
- 7. 【论文阅读】Universal Domain Adaptation
- 8. Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
- 9. 【论文阅读】Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
- 10. 论文阅读Domain Adaptation of DPM models