A deep domain adaption approach for object recognition using Multiple Model Consistency analysis

Abstract

Domain adaption tends to transfer knowledge across domains following dissimilar distribution and where target domain has inadequate labelled samples. When knowledge is transferred from abundantly irrelevant sources negative transfer may occur resulting in poor classification of test samples. Deep learning research illustrates the semantic clustering as well as transferability of deep convolutional features for numerous tasks including domain adaption. Traditional clustering based domain adaption approaches are practical to handle negative transfer scenario. This paper presents a scheme that uses graph based consistency analysis of one supervised and another unsupervised model to effectively transfer knowledge using deep features. This approach uses local neighbourhood analysis to classify hard samples that are identified using consistency analysis of models. This method yields encouraging experimental results on benchmark domain adaption dataset compared to a single deep feature based supervised support vector machine classifier, demonstrating effective use of target domain data.