Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <s> We describe an approach to domain adaptation that is appropriate exactly in the case when one has enough ``target'' data to do slightly better than just using only ``source'' data. Our approach is incredibly simple, easy to implement as a preprocessing step (10 lines of Perl!) and outperforms state-of-the-art approaches on a range of datasets. Moreover, it is trivially extended to a multi-domain adaptation problem, where one has data from a variety of different domains. <s> BIB001 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <s> This paper investigates a new machine learning strategy called translated learning. Unlike many previous learning tasks, we focus on how to use labeled data from one feature space to enhance the classification of other entirely different learning spaces. For example, we might wish to use labeled text data to help learn a model for classifying image data, when the labeled images are difficult to obtain. An important aspect of translated learning is to build a "bridge" to link one feature space (known as the "source space") to another space (known as the "target space") through a translator in order to migrate the knowledge from source to target. The translated learning solution uses a language model to link the class labels to the features in the source spaces, which in turn is translated to the features in the target spaces. Finally, this chain of linkages is completed by tracing back to the instances in the target spaces. We show that this path of linkage can be modeled using a Markov chain and risk minimization. Through experiments on the text-aided image classification and cross-language classification tasks, we demonstrate that our translated learning framework can greatly outperform many state-of-the-art baseline methods. <s> BIB002 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <s> This paper presents a co-regularization based approach to semi-supervised domain adaptation. Our proposed approach (EA++) builds on the notion of augmented space (introduced in EASYADAPT (EA) [1]) and harnesses unlabeled data in target domain to further assist the transfer of information from source to target. This semi-supervised approach to domain adaptation is extremely simple to implement and can be applied as a pre-processing step to any supervised learner. Our theoretical analysis (in terms of Rademacher complexity) of EA and EA++ show that the hypothesis class of EA++ has lower complexity (compared to EA) and hence results in tighter generalization bounds. Experimental results on sentiment analysis tasks reinforce our theoretical findings and demonstrate the efficacy of the proposed method when compared to EA as well as few other representative baseline approaches. <s> BIB003 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <s> We propose a visual event recognition framework for consumer videos by leveraging a large amount of loosely labeled web videos (e.g., from YouTube). Observing that consumer videos generally contain large intraclass variations within the same type of events, we first propose a new method, called Aligned Space-Time Pyramid Matching (ASTPM), to measure the distance between any two video clips. Second, we propose a new transfer learning method, referred to as Adaptive Multiple Kernel Learning (A-MKL), in order to 1) fuse the information from multiple pyramid levels and features (i.e., space-time features and static SIFT features) and 2) cope with the considerable variation in feature distributions between videos from two domains (i.e., web video domain and consumer video domain). For each pyramid level and each type of local features, we first train a set of SVM classifiers based on the combined training set from two domains by using multiple base kernels from different kernel types and parameters, which are then fused with equal weights to obtain a prelearned average classifier. In A-MKL, for each event class we learn an adapted target classifier based on multiple base kernels and the prelearned average classifiers from this event class or all the event classes by minimizing both the structural risk functional and the mismatch between data distributions of two domains. Extensive experiments demonstrate the effectiveness of our proposed framework that requires only a small number of labeled consumer videos by leveraging web data. We also conduct an in-depth investigation on various aspects of the proposed method A-MKL, such as the analysis on the combination coefficients on the prelearned classifiers, the convergence of the learning algorithm, and the performance variation by using different proportions of labeled consumer videos. Moreover, we show that A-MKL using the prelearned classifiers from all the event classes leads to better performance when compared with A-MK- using the prelearned classifiers only from each individual event class. <s> BIB004 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <s> We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications. <s> BIB005 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <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> BIB006 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <s> In this paper, we propose a new framework called domain adaptation machine (DAM) for the multiple source domain adaption problem. Under this framework, we learn a robust decision function (referred to as target classifier) for label prediction of instances from the target domain by leveraging a set of base classifiers which are prelearned by using labeled instances either from the source domains or from the source domains and the target domain. With the base classifiers, we propose a new domain-dependent regularizer based on smoothness assumption, which enforces that the target classifier shares similar decision values with the relevant base classifiers on the unlabeled instances from the target domain. This newly proposed regularizer can be readily incorporated into many kernel methods (e.g., support vector machines (SVM), support vector regression, and least-squares SVM (LS-SVM)). For domain adaptation, we also develop two new domain adaptation methods referred to as FastDAM and UniverDAM. In FastDAM, we introduce our proposed domain-dependent regularizer into LS-SVM as well as employ a sparsity regularizer to learn a sparse target classifier with the support vectors only from the target domain, which thus makes the label prediction on any test instance very fast. In UniverDAM, we additionally make use of the instances from the source domains as Universum to further enhance the generalization ability of the target classifier. We evaluate our two methods on the challenging TRECIVD 2005 dataset for the large-scale video concept detection task as well as on the 20 newsgroups and email spam datasets for document retrieval. Comprehensive experiments demonstrate that FastDAM and UniverDAM outperform the existing multiple source domain adaptation methods for the two applications. <s> BIB007 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <s> Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. To cope with the considerable change between feature distributions of different domains, we propose a new cross-domain kernel learning framework into which many existing kernel methods can be readily incorporated. Our framework, referred to as Domain Transfer Multiple Kernel Learning (DTMKL), simultaneously learns a kernel function and a robust classifier by minimizing both the structural risk functional and the distribution mismatch between the labeled and unlabeled samples from the auxiliary and target domains. Under the DTMKL framework, we also propose two novel methods by using SVM and prelearned classifiers, respectively. Comprehensive experiments on three domain adaptation data sets (i.e., TRECVID, 20 Newsgroups, and email spam data sets) demonstrate that DTMKL-based methods outperform existing cross-domain learning and multiple kernel learning methods. <s> BIB008 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <s> The same object can be observed at different viewpoints or even by different sensors, thus generating multiple distinct even heterogeneous samples. Nowadays, more and more applications need to recognize object from distinct views. Some seminal works have been proposed for object recognition across two views and applied to multiple views in some inefficient pairwise manner. In this paper, we propose a Multi-view Discriminant Analysis (MvDA) method, which seeks for a discriminant common space by jointly learning multiple view-specific linear transforms for robust object recognition from multiple views, in a non-pairwise manner. Specifically, our MvDA is formulated to jointly solve the multiple linear transforms by optimizing a generalized Rayleigh quotient, i.e., maximizing the between-class variations and minimizing the within-class variations of the low-dimensional embeddings from both intra-view and inter-view in the common space. By reformulating this problem as a ratio trace problem, an analytical solution can be achieved by using the generalized eigenvalue decomposition. The proposed method is applied to three multi-view face recognition problems: face recognition across poses, photo-sketch face recognition, and Visual (VIS) image vs. Near Infrared (NIR) image face recognition. Evaluations are conducted respectively on Multi-PIE, CUFSF and HFB databases. Intensive experiments show that MvDA can achieve a more discriminant common space, with up to 13% improvement compared with the best known results. <s> BIB009 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <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> BIB010 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <s> Recent domain adaptation methods successfully learn cross-domain transforms to map points between source and target domains. Yet, these methods are either restricted to a single training domain, or assume that the separation into source domains is known a priori. However, most available training data contains multiple unknown domains. In this paper, we present both a novel domain transform mixture model which outperforms a single transform model when multiple domains are present, and a novel constrained clustering method that successfully discovers latent domains. Our discovery method is based on a novel hierarchical clustering technique that uses available object category information to constrain the set of feasible domain separations. To illustrate the effectiveness of our approach we present experiments on two commonly available image datasets with and without known domain labels: in both cases our method outperforms baseline techniques which use no domain adaptation or domain adaptation methods that presume a single underlying domain shift. <s> BIB011 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <s> Over the last years, several authors have signaled that state of the art categorization methods fail to perform well when trained and tested on data from different databases. The general consensus in the literature is that this issue, known as domain adaptation and/or dataset bias, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. The large majority of these works use BOW feature descriptors, and learning methods based on image-to-image distance functions. Following the seminal work of [6], in this paper we challenge these two assumptions. We experimentally show that using the NBNN classifier over existing domain adaptation databases achieves always very strong performances. We build on this result, and present an NBNN-based domain adaptation algorithm that learns iteratively a class metric while inducing, for each sample, a large margin separation among classes. To the best of our knowledge, this is the first work casting the domain adaptation problem within the NBNN framework. Experiments show that our method achieves the state of the art, both in the unsupervised and semi-supervised settings. <s> BIB012 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <s> We consider the problem of learning a classifier when we dispose little training data from the target domain but abundant training data from several source domains. We make two contributions to the domain adaptation problem. First we extend the Nearest Class Mean (NCM) classifier by introducing for each class domain-dependent mean parameters as well as domain-specific weights. Second, we propose a generic adaptive semi-supervised metric learning technique that iteratively curates the training set by adding unlabeled samples with high prediction confidence and by removing labeled samples for which the prediction confidence is low. These two complementary techniques are evaluated on two public benchmarks: the ImageClef Domain Adaptation Challenge and the Office-CalTech datasets. Both contributions are shown to yield improvements and to be complementary to each other. <s> BIB013 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <s> In this work, we propose a new framework for recognizing RGB images captured by the conventional cameras by leveraging a set of labeled RGB-D data, in which the depth features can be additionally extracted from the depth images. We formulate this task as a new unsupervised domain adaptation (UDA) problem, in which we aim to take advantage of the additional depth features in the source domain and also cope with the data distribution mismatch between the source and target domains. To effectively utilize the additional depth features, we seek two optimal projection matrices to map the samples from both domains into a common space by preserving as much as possible the correlations between the visual features and depth features. To effectively employ the training samples from the source domain for learning the target classifier, we reduce the data distribution mismatch by minimizing the Maximum Mean Discrepancy (MMD) criterion, which compares the data distributions for each type of feature in the common space. Based on the above two motivations, we propose a new SVM based objective function to simultaneously learn the two projection matrices and the optimal target classifier in order to well separate the source samples from different classes when using each type of feature in the common space. An efficient alternating optimization algorithm is developed to solve our new objective function. Comprehensive experiments for object recognition and gender recognition demonstrate the effectiveness of our proposed approach for recognizing RGB images by learning from RGB-D data. <s> BIB014 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <s> In this paper, we present an efficient multi-class heterogeneous domain adaptation method, where data from source and target domains are represented by heterogeneous features of different dimensions. Specifically, we propose to reconstruct a sparse feature transformation matrix to map the weight vector of classifiers learned from the source domain to the target domain. We cast this learning task as a compressed sensing problem, where each binary classifier induced from multiple classes can be deemed as a measurement sensor. Based on the compressive sensing theory, the estimation error of the transformation matrix decreases with the increasing number of classifiers. Therefore, to guarantee reconstruction performance, we construct sufficiently many binary classifiers based on the error correcting output coding. Extensive experiments are conducted on both a toy dataset and three real-world datasets to verify the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy. <s> BIB015 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Class-based Criterion. <s> Domain adaptation methods aim to learn a good prediction model in a label-scarce target domain by leveraging labeled patterns from a related source domain where there is a large amount of labeled data. However, in many practical domain adaptation learning scenarios, the feature distribution in the source domain is different from that in the target domain. In the extreme, the two distributions could differ completely when the feature representation of the source domain is totally different from that of the target domain. To address the problems of substantial feature distribution divergence across domains and heterogeneous feature representations of different domains, we propose a novel feature space independent semi-supervised kernel matching method for domain adaptation in this work. Our approach learns a prediction function on the labeled source data while mapping the target data points to similar source data points by matching the target kernel matrix to a submatrix of the source kernel matrix based on a Hilbert Schmidt Independence Criterion. We formulate this simultaneous learning and mapping process as a non-convex integer optimization problem and present a local minimization procedure for its relaxed continuous form. We evaluate the proposed kernel matching method using both cross domain sentiment classification tasks of Amazon product reviews and cross language text classification tasks of Reuters multilingual newswire stories. Our empirical results demonstrate that the proposed kernel matching method consistently and significantly outperforms comparison methods on both cross domain classification problems with homogeneous feature spaces and cross domain classification problems with heterogeneous feature spaces. <s> BIB016
Feature Representation-based Transfer. Some researches extend distance-based classifiers, such as the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers, from a metric learning perspective to address domain adaptation. BIB012 present an NBNN-based domain adaptation algorithm that learns iteratively a class metric while inducing a large margin separation among classes for each sample. Similarly, BIB013 extend the Nearest Class Mean (NCM) classifier by introducing for each class domain-dependent mean parameters as well as domain-specific weights. Then they propose a generic adaptive semi-supervised metric learning technique that iteratively curates the training set. BIB003 extended fully supervised EASYADAPT method BIB001 to semi-supervised setting, where the unlabelled data are utilized to coregularize these learned hypotheses by making them agree on unlabelled samples. Classifier-based Transfer. BIB007 extend SVM based classifier transfer methods with unlabelled target data. They proposed a domain-dependent regularizer which enforces that the learned target classifiers and the prelearned source classifiers should have similar decision values on the unlabelled target instances. Hybrid Knowledge Transfer. There is also a group of multiple kernel learning (MKL) based transfer methods BIB004 BIB008 ], which learn a new feature representation that simultaneously reduce the distribution shift and optimize the target classifier, using both the statistic criterion and class-based criterion. BIB004 propose a Adaptive Multiple Kernel Learning (A-MKL) cross-domain learning method. A-MKL learns a kernel function and a classifier by minimizing both the structural risk functional and the distribution mismatch between domains using MMD criterion. In addition to A-MKL, BIB008 propose Domain Transfer Multiple Kernel Learning (DTMKL), where an additional regularization terms are proposed to enforce that the decision values from the target classifier and the existing base classifiers are similar on the unlabelled target patterns and the decision values from the target classifier on the labelled target data are close to the true labels. 3.2.4. Self Labelling. Hybrid Knowledge Transfer. The instance weights and feature representation can be exploited simultaneously in the self labelling framework. For example, propose a co-training method in a self-training framework, which bridges the gap between source and target domains by slowly adding to the training set both the target features and instances, such that the current algorithm is the most confident. Then select a subset of shared source and target features based on their compatibility. Lastly, to better exploit the domain specific feature in the unlabelled target domain, the pseudo multiview co-training method is used to add additional features to the training set. Feature Representation-based Transfer. The class-based criterion has also been used to connect heterogeneous feature spaces. Finding relationship between different feature spaces can be seen as translating between different languages. Hence, BIB002 propose a translator using a language model to translate between source and target feature spaces using the class labels. They argue that though the limited number of labelled data in the target domain is not sufficient for building a good classifier, an effective translator can be constructed. BIB009 propose a multi-view discriminant analysis by seeking for a discriminant common space by jointly learning multiple view-specific linear transformations using label information. Manifold alignment method is also used for heterogeneous domain adaptation with the class-based criterion. For example, BIB005 propose to make use of class label information to align the manifolds for heterogeneous domain adaptation. They treat each input domain as a manifold. The goal is to find the respective mappings for each domain to project them into a latent space where the topology, i.e. the manifold structure, is preserved and the discriminative information of each domain is also preserved. The feature augmentation based method has also been proposed BIB010 BIB014 , which transforms the data from two domains into a common subspace, then two new feature mapping functions are proposed to augment the transformed data with their original features and zeros. Similar to the case  in the homogeneous feature spaces as mentioned in Section 3.1.1, a line of research assumes the label spaces of target training set and target test set are non overlapping subsets of source label space. BIB006 extend  ] to learn an asymmetric non-linear transformation that maps points from one domain to another domain using supervised data from both domains. BIB011 extend BIB006 ] to multi-domain adaptation by discovering latent domains with heterogeneous but unknown structure. Classifier-based Transfer. Instead of using the idea of metric learning to learn the asymmetric feature transformation between heterogeneous features BIB006 , the asymmetric metric of classifiers can also be learnt to bridge source and target classifiers on heterogeneous features. For instance, BIB015 propose to learn the feature mapping across heterogeneous features by maximizing the dependency between the mapped source and target classifier weight vectors. In their formulation, the transformation matrix is constrained to be sparse and class-invariant. Feature Representation-based Transfer. BIB016 propose a kernel matching method for heterogeneous domain adaptation. The target data points are mapped to similar source data points by matching the target kernel matrix to a submatrix of the source kernel matrix using the label information. Specifically, The labelled target samples perform as pivot points for class separation, where each labelled target sample is mapped into a source sample with the same class label. Then the unlabelled target instances are expected to be mapped to the source samples with same labels with the guides of labelled pivot points through a distance measure between samples.