Source Hypothesis Transfer for Unsupervised Domain Adaptation ICML 2020 链接:https://arxiv.org/pdf/2002.08546.pdf 这篇文章算是比较早期做source free的工作了,后面的工作基本上都会以这篇作为baseline。它的方法也是非常简单:源模型(source model)由一个feature extractor和一个classifier构成,它的方法就是固定住c...
Unsupervised domain adaptation (UDA) has achieved great success in handling cross-domain machine learning applications.It typically benefits the model training of unlabeled target domain by leveraging knowledge from labeled source domain.For this purpose,the minimization of the marginal distribution ...
LPG 使用的概率分布GMM, MPPL的分值使用模型的model-wise. 也就是本来source分类器和GMM预测的整体融合。相当于一个智慧的mixup. 这个创新没有加入对比学习那样效果好,我猜测,也就是把原来的SHOT后期的聚类修改为GMM. 图2 效果图发布于 2022-10-18 15:34...
GITHUB:https://github.com/youngryan1993/PrDA-Progressive-Domain-Adaptation-from-a-Source-Pre-trained-Model Noisy Label(Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation) GITHUB: 4.加权机制 usfda(Universal Source-Free Domain Adaptation) GITHUB:https://github.com/val-...
GITHUB:https://github.com/youngryan1993/PrDA-Progressive-Domain-Adaptation-from-a-Source-Pre-trained-Model Noisy Label(Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation) GITHUB: 4.加权机制 usfda(Universal Source-Free Domain Adaptation) ...
知道类别数，模型良好初始化）的unsupervised clustering问题。没有source data其实就不用直接解决domain ...
Unsupervised domain adaptation (UDA) is one of the key technologies to solve a problem where it is hard to obtain ground truth labels needed for supervised learning. In general, UDA assumes that all samples from source and target domains are available during the training process. However, this ...
Paper tables with annotated results for Enhancing and Adapting in the Clinic: Source-free Unsupervised Domain Adaptation for Medical Image Enhancement
上图展示了source-free domain adaptation和一般的DA的区别。在之前的两篇source-free的论文中已经反复讲解,不再赘述。 1 方法 这文章也是使用Positive learning和Negative Learning的方法。 方法名称:Source-Free domain adaptive Semantic Segmentation (SFSS) ...
[CVPR'24]Source-Free Domain Adaptation with Frozen Multimodal Foundation Model (DIFO) [IJCV'23]Source-Free Domain Adaptation via Target Prediction Distribution Searching (TPDS) [NN'22]Semantic consistency learning on manifold for source data-free unsupervised domain adaptation (SCLM) ...