A survey on heterogeneous transfer learning <s> MMDT <s> In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features can change from one dataset to the next. In this paper, we address the problem of visual domain adaptation for transferring object models from one dataset or visual domain to another. We introduce ARC-t, a flexible model for supervised learning of non-linear transformations between domains. Our method is based on a novel theoretical result demonstrating that such transformations can be learned in kernel space. Unlike existing work, our model is not restricted to symmetric transformations, nor to features of the same type and dimensionality, making it applicable to a significantly wider set of adaptation scenarios than previous methods. Furthermore, the method can be applied to categories that were not available during training. We demonstrate the ability of our method to adapt object recognition models under a variety of situations, such as differing imaging conditions, feature types and codebooks. <s> BIB001 </s> A survey on heterogeneous transfer learning <s> MMDT <s> We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods. <s> BIB002 </s> A survey on heterogeneous transfer learning <s> MMDT <s> Abstract: We present an algorithm that learns representations which explicitly compensate for domain mismatch and which can be efficiently realized as linear classifiers. Specifically, we form a linear transformation that maps features from the target (test) domain to the source (training) domain as part of training the classifier. We optimize both the transformation and classifier parameters jointly, and introduce an efficient cost function based on misclassification loss. Our method combines several features previously unavailable in a single algorithm: multi-class adaptation through representation learning, ability to map across heterogeneous feature spaces, and scalability to large datasets. We present experiments on several image datasets that demonstrate improved accuracy and computational advantages compared to previous approaches. <s> BIB003
Hoffman et al. BIB003 proposed a heterogeneous domain adaptation method for multi-class image classification called Max-Margin Domain Transforms (MMDT). This method allows for multiple classes, requires limited labeled target training instances, and utilizes unlabeled target instances though one can have classes which contain only unlabeled instances. This is possible as it adapts all the points through a linear asymmetric feature transformation to map the target domain to that of the source creating a feature transformation shared across all classes. To establish multi-class support, the proposed method adapts a max-margin classifier by learning a shared component of the domain shift though the feature transformation. MMDT simultaneously learns the projection matrices along with the classifier parameters and performs optimization on both through a classification loss based cost function. In other words, in this framework both the classification objective and the feature transformation are updated together based on the prediction results of the previous training instance in relation to its truth label. By performing such optimization through classification loss, accuracy can be increased. This method also provides for less computational cost as it reduces training time by using hyperplane, rather than similarity, constraints. By doing so, one can optimize in linear space and avoid computational complexities of kernel techniques, thus making the proposed method suitable for scaling to larger quantities of training data. The main idea behind this method is to jointly learn affine hyperplanes which separate the classes consisting of source and target instances. This is done while learning the feature transformation to map the points of the target to the source such that it projects the points onto the correct side of each source hyperplane. In comparison, ARC-t BIB001 uses similarity constraints to map points of the same category close to each other followed by a classification step but, on the other hand, MMDT proposes to project the target points to the correct side of the already learned hyperplane as for better classification accuracy. Experiments were performed to test the performance of the proposed method for image classification tasks. Four tests were conducted which include multi-class, homogeneous domain adaption, heterogeneous domain adaption, multi-task DA, as well as scaling the test to larger datasets for computational performance analysis. For the heterogeneous tests, the proposed MMDT was compared with ARC-t BIB001 , HFA BIB002 , and SVM trained only on the target data. The results showed that the proposed method was effective and it had the highest classification accuracy in most cases, while also having the fastest computation time for the larger dataset test while maintaining highest accuracy.