A survey on heterogeneous transfer learning <s> HMCA <s> When a trained classifier on specific domain (source domain) is applied in a different domain (target domain) the accuracy is degraded significantly. The main reason for this degradation is the distribution difference between the source and target domains. Domain adaptation aims to lessen this accuracy degradation. In this paper, we focus on adaptation for heterogeneous domains (where the source and target domain may have different feature spaces) and propose a novel algorithm which uses the pre-learned source classifier to adapt a trained target classifier. In this method, a max-margin classifier is trained on the target data and is adapted using the offset of the source classifier. The main strength of this adaptation is its low complexity and high speed which makes it a proper adaptation choice for problems with large-size datasets such as object detection. We test our method on human detection datasets and the experimental results show the significant improvement in accuracy, in comparison to several baselines. <s> BIB001 </s> A survey on heterogeneous transfer learning <s> HMCA <s> In many real classification scenarios the distribution of test (target) domain is different from the training (source) domain. The distribution shift between the source and target domains may cause the source classifier not to gain the expected accuracy on the target data. Domain adaptation has been introduced to solve the accuracy-dropping problem caused by distribution shift phenomenon between domains. In this paper, we study model-transferring methods as a practical branch of adaptation methods, which adapt the source classifier to new domains without using the source samples. We introduce a new SVM-based model-transferring method, in which a max-margin classifier is trained on labeled target samples and is adapted using the offset of the source classifier. We call it Heterogeneous Max-Margin Classifier Adaptation Method, abbreviated as HMCA. The main strength of HMCA is its applicability for heterogeneous domains where the source and target domains may have different feature types. This property is important because the previously proposed model-transferring methods do not provide any solution for heterogeneous problems. We also introduce a new similarity metric that reliably measures adaptability between two domains according to HMCA structure. In the situation that we have access to several source classifiers, the metric can be used to select the most appropriate one for adaptation. We test HMCA on two different computer vision problems (pedestrian detection and image classification). The experimental results show the advantage in accuracy rate for our approach in comparison to several baselines. We propose a new SVM-based model-transferring method for adaptation.Our method applies adaptation in the one-dimensional discrimination space.The proposed method can handle heterogeneous domains.Based on proposed model-transferring method, we design a new metric for measuring the adaptability between two domains. <s> BIB002
Mozafari et al. BIB002 proposed a framework called Heterogeneous Max-margin Classifier Adaptation (HMCA) which addresses heterogeneous and homogeneous domain adaptation problems. This method uses model-transferring for these DA tasks and is an extension from their previous work BIB001 . Model-transferring domain adaptation methods use a previously trained source classifier for adaptation purposes on a target domain. These methods require trained source classifiers and the target data during training thus the source training samples are not required for building the classifier in the target. This is because, in this case, the parameters from the source classifier are used in the adaptation process rather than the source samples. HMCA learns a max-margin classifier for the target domain, and adapts it according to the source classifier's pre-learned offset. (1) This adaption occurs through a modification in the target's SVM objective function which minimizes the distance of the target's offset from the source's offset. To do this, the source classifier and the labeled target domain data are projected onto a one-dimensional space. This offset can be seen as the discrimination point which the classifier uses to separate and distinguish the two classes in the one-dimensional space. Thus, to utilize source knowledge and adapt the target classifier utilizing the source model, a linear SVM is found whose offset discriminates the target samples correctly in the one-dimensional space. This is done under the condition that it also must have the minimum distance to the offset of the source as to maximize the similarity between the domains. Since only the classifier offset is required from the source domain to adapt for the target, this method works for domains represented by heterogeneous features. Even so, HCMA has limitations though as it faces issues with overfitting (when there is high dimensionality and low target samples), noise, and outliers. It also makes two assumptions. The first is that the source and target classifiers be built with the same ratio of positive and negative samples, an assumption which may not always hold in real-world situations. The second is that the two domains hold an Intra-Sample Distance Pattern (ISDP) condition. ISDP is used here as a measure of correspondence between samples in the source and target domains. Two domains with similar ISDP mean they are highly similar as a sample from one domain corresponds to that of another. In other words, it is assumed that the source and target have similar conditional distributions in the onedimensional space. This ISDP assumption does not always hold in real-world scenarios, though it does act as a method to measure the domain adaptability of source classifiers as to select the most similar source domain to that of the target when multiple source classifiers are available. Experiments were conducted under homogeneous and heterogeneous scenarios and the proposed HMCA showed increased accuracy rates for pedestrian detection and image classification tasks.