 Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images . This issue is commonly referred to as covariate shift or dataset bias. Domain adaptation attempts to address this problemby leveraging domain shift characteristics from labeled data in a related domain when learning a classifier for label-scarce target dataset. There are a plethora of works to adapt object classification and semantic segmentation models to label-scarce target dataset through unsupervised domain adaptation.
<clarity>  Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images . This issue is commonly referred to as covariate shift or dataset bias. Domain adaptation attempts to address this problemby leveraging domain shift characteristics from labeled data in a related domain when learning a classifier for label-scarce target dataset. There are a plethora of works to adapt object classification and semantic segmentation models to label-scarce target dataset through unsupervised domain adaptation.
 Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images , termed as domain adaptation problem. There is a plethora of works to adapt object classification and semantic segmentation models to label-scarce target dataset through unsupervised domain adaptation.