Domain adaptation and generalization for visual recognition
Li Niu · 2017
In many visual recognition tasks, the domain distribution mismatch between the training set (i.e., source domain) and the test set (i.e., target domain) may cause the performance of the classifier learnt from the training set to be significantly degraded on the test set.The solutions to address the domain distribution mismatch can be classified into Domain Adaptation (DA) and Domain Generalization (DG).Specifically, DA utilizes the unlabeled target domain data in the training phase to reduce the domain distribution mismatch while DG aims to learn the classifier on the source domain which can generalize well to any unseen target domain.This thesis focuses on DA and DG for visual recognition.Most of the existing DA and DG approaches require well labeled training data.Since collecting labeled data is often time consuming and expensive, some recent works utilize freely available web images/videos for visual recognition.Therefore, the DA and DG methods can be categorized based on learning from web data or well labeled data.For learning from web data, besides the data distribution mismatch between the web training data and test data, there also exist some other problems such as label noise of web data and extra information associated with web data (i.e., privileged information).All the existing DA and DG methods only consider the domain distribution mismatch, but ignore the label noise and privileged information.To this end, we propose a DA framework and a DG method for learning from web data, which leads to the first and second work in this thesis respectively.In the first work, we propose our DA framework named Domain Adaptive Multi-Instance Learning using Privileged Information (MIL-PI-DA) for visual recognition by learning from web data, which can handle the label noise, utilize the privileged information, and reduce the domain distribution mismatch at the same time.i