Domain adaptation techniques aim at adapting a classifier learnt on a source domain to work on the target domain. Exploiting the subspaces spanned by features of the source and target domains respectively is one approach that has been investigated towards solving this problem. These techniques normally assume the existence of a single subspace for the entire source target domain. In this work, we consider the hierarchical organization of the data and consider multiple subspaces for the source and target domain based on the hierarchy. We evaluate different subspace based domain adaptation techniques under this setting and observe that using different subspaces based on the hierarchy yields consistent improvement over a non-hierarchical baseline
@cite_6: A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains. Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM). As proof of concept we use HA-SSVM for pedestrian detection and object category recognition. In the former we apply HA-SSVM to the deformable part-based model (DPM) while in the latter HA-SSVM is applied to multi-category classifiers. In both cases, we show how HA-SSVM is effective in increasing the detection recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain structure discovery for object category recognition.
@cite_3: Complex visual data contain discriminative structures that are difficult to be fully captured by any single feature descriptor. While recent work on domain adaptation focuses on adapting a single hand-crafted feature, it is important to perform adaptation of a hierarchy of features to exploit the richness of visual data. We propose a novel framework for domain adaptation using a sparse and hierarchical network (DASH-N). Our method jointly learns a hierarchy of features together with transformations that rectify the mismatch between different domains. The building block of DASH-N is the latent sparse representation. It employs a dimensionality reduction step that can prevent the data dimension from increasing too fast as one traverses deeper into the hierarchy. The experimental results show that our method compares favorably with the competing state-of-the-art methods. In addition, it is shown that a multi-layer DASH-N performs better than a single-layer DASH-N.
@cite_8: 
Only few works have looked at the use of hierarchies in the context of domain adaptation. In @cite_3 , Nguyen propose to adapt a hierarchy of features to exploit the richness of visual data. The intent behind this work is similar to our work, in that semantic closeness and context information are exploited to boost domain adaptation performance. Taking this idea forward a recent work on hierarchical adaptive structural SVM for domain adaptation has been proposed in @cite_6 . They organize multiple target domains into a hierarchical structure (tree) and adapt the source model to them jointly. Others have used statistical methods for hierarchical domain adaptation, e.g. in @cite_8 a hierarchical Bayesian prior is used to solve the domain shift problem in natural language and speech processing. However, the previous works have assumed a single common subspace between source and target, while our approach makes use of the hierarchical structure among the different classes to learn separate subspaces.