%0 Generic
%T Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest
%A Azadeh S. Mozafari
%A David Vazquez
%A Mansour Jamzad
%A Antonio Lopez
%D 2016
%F Azadeh S. Mozafari2016
%O ADAS
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2868), last updated on Thu, 28 Jan 2021 10:26:56 +0100
%X Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved.
%K Domain Adaptation
%K Pedestrian detection
%K Random Forest
%9 miscellaneous
%U http://refbase.cvc.uab.es/files/mvj2016.pdf