Transfer Learning for Cross-Dataset Recognition: A Survey <s> Hybrid 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> Hybrid Criterion. <s> Labeled examples are often expensive and time-consuming to obtain. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have (a) different feature spaces (e.g., image vs. text data), (b) different data distributions, and (c) different output spaces? This paper proposes a solution and discusses the conditions where this is possible and highly likely to produce better results. It works by first using spectral embedding to unify the different feature spaces of the target and source data sets, even when they have completely different feature spaces. The principle is to cast into an optimization objective that preserves the original structure of the data, while at the same time, maximizes the similarity between the two. Second, a judicious sample selection strategy is applied to select only those related source examples. At last, a Bayesian-based approach is applied to model the relationship between different output spaces. The three steps can bridge related heterogeneous sources in order to learn the target task. Among the 12 experiment data sets, for example, the images with wavelet-transformed-based features are used to predict another set of images whose features are constructed from color-histogram space. By using these extracted examples from heterogeneous sources, the models can reduce the error rate by as much as~50\%, compared with the methods using only the examples from the target task. <s> BIB002 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Hybrid Criterion. <s> Effectively utilizing readily available auxiliary data to improve predictive performance on new modeling tasks is a key problem in data mining. In this research the goal is to transfer knowledge between sources of data, particularly when ground truth information for the new modeling task is scarce or is expensive to collect where leveraging any auxiliary sources of data becomes a necessity. Towards seamless knowledge transfer among tasks, effective representation of the data is a critical but yet not fully explored research area for the data engineer and data miner. Here we present a technique based on the idea of sparse coding, which essentially attempts to find an embedding for the data by assigning feature values based on subspace cluster membership. We modify the idea of sparse coding by focusing the identification of shared clusters between data when source and target data may have different distributions. In our paper, we point out cases where a direct application of sparse coding will lead to a failure of knowledge transfer. We then present the details of our extension to sparse coding, by incorporating distribution distance estimates for the embedded data, and show that the proposed algorithm can overcome the shortcomings of the sparse coding algorithm on synthetic data and achieve improved predictive performance on a real world chemical toxicity transfer learning task. <s> BIB003 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Hybrid 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> BIB004 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Hybrid Criterion. <s> We present a novel cross-dataset action recognition framework that utilizes relevant actions from other visual domains as auxiliary knowledge for enhancing the learning system in the target domain. The data distribution of relevant actions from a source dataset is adapted to match the data distribution of actions in the target dataset via a cross-domain discriminative dictionary learning method, through which a reconstructive, discriminative and domain-adaptive dictionary-pair can be learned. Using selected categories from the HMDB51 dataset as the source domain actions, the proposed framework achieves outstanding performance on the UCF YouTube dataset. <s> BIB005 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Hybrid Criterion. <s> Data-driven dictionaries have produced state-of-the-art results in various classification tasks. However, when the target data has a different distribution than the source data, the learned sparse representation may not be optimal. In this paper, we investigate if it is possible to optimally represent both source and target by a common dictionary. Specifically, we describe a technique which jointly learns projections of data in the two domains, and a latent dictionary which can succinctly represent both the domains in the projected low-dimensional space. An efficient optimization technique is presented, which can be easily kernelized and extended to multiple domains. The algorithm is modified to learn a common discriminative dictionary, which can be further used for classification. The proposed approach does not require any explicit correspondence between the source and target domains, and shows good results even when there are only a few labels available in the target domain. Various recognition experiments show that the method performs on par or better than competitive state-of-the-art methods. <s> BIB006 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Hybrid Criterion. <s> Discriminative regression models have proved effective for many vision applications (here we focus on 3D full-body and head pose estimation from image and depth data). However, dataset bias is common and is able to significantly degrade the performance of a trained model on target test sets. As we show, covariate shift, a form of unsupervised domain adaptation (USDA), can be used to address certain biases in this setting, but is unable to deal with more severe structural biases in the data. We propose an effective and efficient semi-supervised domain adaptation (SSDA) approach for addressing such more severe biases in the data. Proposed SSDA is a generalization of USDA, that is able to effectively leverage labeled data in the target domain when available. Our method amounts to projecting input features into a higher dimensional space (by construction well suited for domain adaptation) and estimating weights for the training samples based on the ratio of test and train marginals in that space. The resulting augmented weighted samples can then be used to learn a model of choice, alleviating the problems of bias in the data; as an example, we introduce SSDA twin Gaussian process regression (SSDA-TGP) model. With this model we also address the issue of data sharing, where we are able to leverage samples from certain activities (e.g., walking, jogging) to improve predictive performance on very different activities (e.g., boxing). In addition, we analyze the relationship between domain similarity and effectiveness of proposed USDA versus SSDA methods. Moreover, we propose a computationally efficient alternative to TGP (Bo and Sminchisescu 2010), and it's variants, called the direct TGP. We show that our model outperforms a number of baselines, on two public datasets: HumanEva and ETH Face Pose Range Image Dataset. We can also achieve 8---15 times speedup in computation time, over the traditional formulation of TGP, using the proposed direct formulation, with little to no loss in performance. <s> BIB007 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Hybrid Criterion. <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> BIB008 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Hybrid Criterion. <s> Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias. Fine-tuning deep models in a new domain can require a significant amount of labeled data, which for many applications is simply not available. We propose a new CNN architecture to exploit unlabeled and sparsely labeled target domain data. Our approach simultaneously optimizes for domain invariance to facilitate domain transfer and uses a soft label distribution matching loss to transfer information between tasks. Our proposed adaptation method offers empirical performance which exceeds previously published results on two standard benchmark visual domain adaptation tasks, evaluated across supervised and semi-supervised adaptation settings. <s> BIB009 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Hybrid Criterion. <s> Data-driven dictionaries have produced the state-of-the-art results in various classification tasks. However, when the target data has a different distribution than the source data, the learned sparse representation may not be optimal. In this paper, we investigate if it is possible to optimally represent both source and target by a common dictionary. In particular, we describe a technique which jointly learns projections of data in the two domains, and a latent dictionary which can succinctly represent both the domains in the projected low-dimensional space. The algorithm is modified to learn a common discriminative dictionary, which can further improve the classification performance. The algorithm is also effective for adaptation across multiple domains and is extensible to nonlinear feature spaces. The proposed approach does not require any explicit correspondences between the source and target domains, and yields good results even when there are only a few labels available in the target domain. We also extend it to unsupervised adaptation in cases where the same feature is extracted across all domains. Further, it can also be used for heterogeneous domain adaptation, where different features are extracted for different domains. Various recognition experiments show that the proposed method performs on par or better than competitive state-of-the-art methods. <s> BIB010 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Hybrid Criterion. <s> Feature learning with deep models has achieved impressive results for both data representation and classification for various vision tasks. Deep feature learning, however, typically requires a large amount of training data, which may not be feasible for some application domains. Transfer learning can be one of the approaches to alleviate this problem by transferring data from data-rich source domain to data-scarce target domain. Existing transfer learning methods typically perform one-shot transfer learning and often ignore the specific properties that the transferred data must satisfy. To address these issues, we introduce a constrained deep transfer feature learning method to perform simultaneous transfer learning and feature learning by performing transfer learning in a progressively improving feature space iteratively in order to better narrow the gap between the target domain and the source domain for effective transfer of the data from source domain to target domain. Furthermore, we propose to exploit the target domain knowledge and incorporate such prior knowledge as constraint during transfer learning to ensure that the transferred data satisfies certain properties of the target domain. To demonstrate the effectiveness of the proposed constrained deep transfer feature learning method, we apply it to thermal feature learning for eye detection by transferring from the visible domain. We also applied the proposed method for cross-view facial expression recognition as a second application. The experimental results demonstrate the effectiveness of the proposed method for both applications. <s> BIB011 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Hybrid Criterion. <s> Heterogeneity of features and lack of correspondence between data points of different domains are the two primary challenges while performing feature transfer. In this paper, we present a novel supervised domain adaptation algorithm (SHDA-RF) that learns the mapping between heterogeneous features of different dimensions. Our algorithm uses the shared label distributions present across the domains as pivots for learning a sparse feature transformation. The shared label distributions and the relationship between the feature spaces and the label distributions are estimated in a supervised manner using random forests. We conduct extensive experiments on three diverse datasets of varying dimensions and sparsity to verify the superiority of the proposed approach over other baseline and state of the art transfer approaches. <s> BIB012 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Hybrid Criterion. <s> In this paper, we propose an approach to the domain adaptation, dubbed Second-or Higher-order Transfer of Knowledge (So-HoT), based on the mixture of alignments of second-or higher-order scatter statistics between the source and target domains. The human ability to learn from few labeled samples is a recurring motivation in the literature for domain adaptation. Towards this end, we investigate the supervised target scenario for which few labeled target training samples per category exist. Specifically, we utilize two CNN streams: the source and target networks fused at the classifier level. Features from the fully connected layers fc7 of each network are used to compute second-or even higher-order scatter tensors, one per network stream per class. As the source and target distributions are somewhat different despite being related, we align the scatters of the two network streams of the same class (within-class scatters) to a desired degree with our bespoke loss while maintaining good separation of the between-class scatters. We train the entire network in end-to-end fashion. We provide evaluations on the standard Office benchmark (visual domains) and RGB-D combined with Caltech256 (depth-to-rgb transfer). We attain state-of-the-art results. <s> BIB013
Feature Representation-based Transfer. The higher-level representation criterion and class-based criterion can be used together for better cross-dataset representation. For example, BIB005 match source and target distributions via a cross-domain discriminative dictionary learning method, where a reconstructive, discriminative and domain-adaptive dictionary-pair are learned, such that the instances of same class from different domains have similar sparse codes representations. BIB006 also propose a dictionary learning based method. They jointly learn projections that map the data in the two domains into a low-dimensional subspace and a common discriminative dictionary which represent data of two domains in the projected subspace. Except for the discriminative dictionary learning, the label information can also be used for guiding the deep neural networks to reduce domain shift. BIB013 utilize two CNN streams: the source and target networks fused at the classifier level. Features from the fully connected layers fc7 of each network are used to compute second-or even higher-order scatter tensors; one per network stream per class. Then the scatters of the two network streams of the same class (within-class scatters) are aligned while good separation of the between-class scatters are maintained. Hence, in addition to higher-level representation criterion and class-based criterion, the statistic criterion is also used in BIB013 . Feature Representation-based Transfer. A large group of feature representationbased methods in this problem combines statistic criterion and class-based criterion. Pan et al. [2011] extend the unsupervised TCA method  ] to semisupervised version by maximizing the label dependence using labelled target data while minimizing the MMD distance using unlabelled target data. proposed a general subspace learning method for semi-supervised domain adaptation. They explore the low-dimensional structures across domains, such that the empirical risk of labelled samples from both domains are minimized, the distance between samples of same category from different domains is small, and the outputs of the predictive function are restricted to have similar values for similar examples. BIB003 modify the idea of sparse coding by focusing the identification of shared clusters between data when source and target data may have different distributions by incorporating distribution distance estimates for the embedded data. The kernel density estimation is used for estimating the distributions and symmetric version of the common KL-divergence measure, the Jensen-Shannon divergence, is used for comparing two distributions. To incorporate target label information, the class-based distribution distance is estimated for each class. BIB009 propose a CNN architecture that simultaneously learns a domain invariant representation using a domain confusion loss (H divergence) over all source and target data and transfers the learned source semantic structure to the target domain via soft labels (which is defined as the average over the softmax of all activations of source examples in each category). Similar to the settings of  , labelled target data from only a subset of the categories of interest are available in the training stage. Recently, BIB011 introduce a constrained deep transfer feature learning method to perform simultaneous transfer learning and feature learning. Specifically, the paired source and target data are used for capturing the joint distribution of target and source data as a bridge between domains. Then additional large amount of unpaired source data are transferred to the target domain as pseudo data for further target domain feature learning. Hence, correspondence criterion and high-lever representation criterion are used. Hybrid Knowledge Transfer. BIB007 generalize the EASYADAPT method BIB001 to semi-supervised setting. They propose to project input features into a higher dimensional space as well as estimate weights for the training samples based on the ratio of test and training marginal distributions in that space using unlabelled target samples. Hence, the class-based criterion and statistic criterion are used for both feature representation and instance re-weighting. Feature Representation-based Transfer. By contrast to the scenario mentioned in Section 4.1.1, where the source and target datasets are different only in terms of feature spaces, another scenario assumes that both the feature spaces and the data distributions are different. BIB010 extend BIB006 ] to heterogeneous feature spaces, where the two projections and a latent dictionary are jointly learnt to simultaneously find a common discriminative low-dimensional space and reduce the distribution shift. Similarly, BIB012 use the shared label distributions present across the domains as pivots for learning a sparse feature transformation. The shared label distributions and the relationship between the feature spaces and the label distributions are estimated in a supervised manner using random forests. Hence, the higher-level representation and class-based criteria are used in both methods. Hybrid Knowledge Transfer. BIB008 extend BIB004 ] to scale well to large dataset by proposing a Max-Margin Domain Transforms (MMDT) method that provides a way to adapt max-margin classifiers and an asymmetric transform jointly to optimize both the feature representation and the classifier parameters. The MMDT can be optimized quickly in linear space. Similar to BIB010 BIB012 , BIB002 also assume both feature spaces and data distributions are different between datasets. They propose to learn a spectral embedding to unify the different feature spaces and use sample selection method to deal with distribution mismatch. Hence, both feature transformation and sample selection are preceded.