Transfer Learning for Cross-Dataset Recognition: A Survey <s> Sequential Labelled Target Data <s> In this paper, we investigate a new machine learning framework called Online Transfer Learning (OTL) that aims to transfer knowledge from some source domain to an online learning task on a target domain. We do not assume the target data follows the same class or generative distribution as the source data, and our key motivation is to improve a supervised online learning task in a target domain by exploiting the knowledge that had been learned from large amount of training data in source domains. OTL is in general challenging since data in both domains not only can be different in their class distributions but can be also different in their feature representations. As a first attempt to this problem, we propose techniques to address two kinds of OTL tasks: one is to perform OTL in a homogeneous domain, and the other is to perform OTL across heterogeneous domains. We show the mistake bounds of the proposed OTL algorithms, and empirically examine their performance on several challenging OTL tasks. Encouraging results validate the efficacy of our techniques. <s> BIB001 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Sequential Labelled Target Data <s> One of the great open challenges in visual recognition is the ability to cope with unexpected stimuli. In this work, we present a technique to interpret detected anomalies and update the existing knowledge of normal situations. The addressed context is the analysis of human behavior in indoor surveillance scenarios, where new activities might need to be learned, once the system is already in operation. Our approach is based on human tracking with multiple activity trackers. The main contribution is to integrate a learning stage, where labeled and unlabeled information is collected and analyzed. To this end we develop a new multi-class version of transfer learning which requires minimal human interaction but still provides semantic labels of the new classes. The activity model is then updated with the new activities. Experiments show promising results. <s> BIB002 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Sequential Labelled Target Data <s> Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain) is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data. <s> BIB003
In real world applications, the target data can be sequential video streams or continuous evolving data. The distribution of the target data may also change with time. Since the target data are labelled, this problem is named supervised online domain adaptation. Classifier-based Methods. BIB003 propose an incremental domain adaptation for object detection by assuming weak-labelling. Specifically, the adaptation model is a weighted ensemble of source and target classifiers and the ensemble weights are updated with time. As mentioned, the target domain data can come sequentially. Hence, this problem assume the target data are sequential and can be from different classes, which is also called online transfer learning, and closely related to lifelong learning. Both concepts focus on the continuous learning processes for evolving tasks. However, the online transfer learning emphasize on the performance on the target data (without sufficient target training data), but lifelong learning tries to improve the future target task (with sufficient target training data) as well as all the past tasks . Also, the lifelong learning can be seen as incremental/online multi-task learning. Classifier-based Transfer. BIB002 address an action recognition scenario where the unseen activities to be recognized only have one labelled sample per new activity, with the help of other offline trained activities with many labelled data. They build a multiclass model which exploits prior knowledge of known classes and incrementally learns the new actions. Then the newly labelled activities are integrated in the previous model to update the activity model. BIB001 propose an ensemble learning based method (OTL) that learns a classifier in an online fashion with data from the target domain, and combines it with the source domain classifier. The weights for the combination are adjusted dynamically on the basis of a loss function which evaluates the difference among the current prediction and the correct label of new incoming sample.  then extended OTL BIB001 and addressed the case of online transfer from multiple sources. This problem (Table XVII) assume the sequential target data have different feature space with source data, which is named as heterogeneous online supervised transfer learning. Classifier-based Transfer. As mentioned in Section 5.3, BIB001 propose the OTL method for online transfer learning. They also consider the case of heterogeneous feature spaces by assuming the feature space of the source domain is a subset of that of the target domain. Then a multi-view approach is proposed by adopting a co-regularization principle of online learning of two target classifiers simultaneously from the two views. The unseen target example is classified by the combination of the two target classifiers.