论文标题
源域子集对半监督域的适应性的源域子集采样
Source Domain Subset Sampling for Semi-Supervised Domain Adaptation in Semantic Segmentation
论文作者
论文摘要
在本文中，我们介绍了源域子集采样（SDSS），作为半监视域适应性的新观点。我们通过对训练源数据中的一个有意义的子集进行采样和利用来提出域的适应。我们的关键假设是整个源域数据可能包含对适应无助的样本。因此，域的适应性可以从仅由有用和相关样本组成的源数据的子集中受益。所提出的方法有效地子样本完整的源数据以生成一个小规模的有意义的子集。因此，培训时间减少，并通过我们的子采样源数据提高了性能。为了进一步验证我们方法的可扩展性，我们构建了一个名为Ocean Ship的新数据集，该数据集包含500个带有地面真实标签的真实和200K合成样品图像。当在GTA5上应用于CityScapes和Synthia，在CityScapes公共基准数据集上应用于CityScapes和Synthia，SDSS取得了最先进的性能，并且通过基线模型对我们的海洋船数据集进行了9.13 MIOU的改进。
In this paper, we introduce source domain subset sampling (SDSS) as a new perspective of semi-supervised domain adaptation. We propose domain adaptation by sampling and exploiting only a meaningful subset from source data for training. Our key assumption is that the entire source domain data may contain samples that are unhelpful for the adaptation. Therefore, the domain adaptation can benefit from a subset of source data composed solely of helpful and relevant samples. The proposed method effectively subsamples full source data to generate a small-scale meaningful subset. Therefore, training time is reduced, and performance is improved with our subsampled source data. To further verify the scalability of our method, we construct a new dataset called Ocean Ship, which comprises 500 real and 200K synthetic sample images with ground-truth labels. The SDSS achieved a state-of-the-art performance when applied on GTA5 to Cityscapes and SYNTHIA to Cityscapes public benchmark datasets and a 9.13 mIoU improvement on our Ocean Ship dataset over a baseline model.
