Open Set Domain Adaptation by Backpropagation(OSBP)论文数字数据集复现 1.准备数据集 2.模型结构 3.训练(SVHN→→ MNIST) 4.结果1|11.准备数据集MNIST数据集:28*28,共70000张图片,10类数字USPS数据集:16*16,共20000张图片,10类数字SVHN数据集:32*32,共73257张图片
Open Set Domain Adaptation by Backpropagation(OSBP)论文数字数据集复现(非官方方法) 1.准备数据集 MNIST数据集:28*28,共70000张图片,10类数字 USPS数据集:16*16,共20000张图片,10类数字 SVHN数据集:32*32,共73257张图片,10类数字 由于torchvision.datasets中自带的数据集没有USPS数据集,所以使用一个类设置数...
作者首先对比了现有的一些深度方法,比如DAN、RTN、BP,然后发现提出的方法不仅在open set,在close set上也很好。然后,提取深度特征后,又对比了TCA、GFK、SA、CORAL这几个方法,仍然是作者的方法好。 文章做了大量的实验,解释了很多open set下进行domain adaptation的规律。详细请参考文章。 总结 这篇文章提出了一个新...
Open Set:目标域与源域的对象类别可能并不都相同,同时,目标域中可能包含了与源域毫无相关的图像(source有5类,target只共享了其中某些类,还有未知类) 例如:b图中除了之前的三类还有unknown类型的数据,而在目标域中也存在源域中不存在的类别的数据 直观理解: 论文中实现Open Set Domain Adaptation的方法: a图中的...
Keywords:DomainAdaptation,OpenSetRecognition,AdversarialLearning 1Introduction Deepneuralnetworkshavedemonstratedsigni,cantperformanceonmanyimagerecognitiontasks[1].Oneofthemainproblemsofsuchmethodsisthatbasically,theycannotrecognizesamplesasunknown,whoseclassisabsentduringtraining.Wecallsuchaclassasan“unknownclass”andthe...
However, factors present in non-controllable environments such as unlabeled datasets with varying levels of domain and category shift can reduce model accuracy. The Open Set Domain Adaptation (OSDA) is a challenging problem that arises when both of these issues occur together. Existing OSDA ...
Open-set domain adaptation (OSDA), which allows the target domain to store invisible class samples in the source domain, has recently received significant attention. In this paper, we propose a new unsupervised OSDA classification framework using an evidential network and multi-binary classifier and ...
A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition open-sourceawesome-listopen-setopen-set-recognitionopen-word-recognitionout-of-distribution-detectionopen-set-domain-adaptation ...
Code and data released for paper "Imbalanced Open Set Domain Adaptation via Moving-threshold Estimation and Gradual Alignment" - mendicant04/OMEGA
Unsupervised domain adaptation (UDA) has received significant attention in medical image analysis when labels are only available for the source domain data but not for the target domain. Previous UDA methods mainly focused on the closed-set scenario, ass