Co-regularized Alignment for Unsupervised Domain Adaptation

Deep neural networks, trained with large amount of labeled data, can fail to generalize well when tested with examples from a \emph{target domain} whose distribution differs from the training data distribution, referred as the \emph{source domain}. It can be expensive or even infeasible to obtain required amount of labeled data in all possible domains. Unsupervised domain adaptation sets out to address this problem, aiming to learn a good predictive model for the target domain using labeled examples from the source domain but only unlabeled examples from the target domain. Domain alignment approaches this problem by matching the source and target feature distributions, and has been used as a key component in many state-of-the-art domain adaptation methods. However, matching the marginal feature distributions does not guarantee that the corresponding class conditional distributions will be aligned across the two domains. We propose co-regularized domain alignment for unsupervised domain adaptation, which constructs multiple diverse feature spaces and aligns source and target distributions in each of them individually, while encouraging that alignments agree with each other with regard to the class predictions on the unlabeled target examples. The proposed method is generic and can be used to improve any domain adaptation method which uses domain alignment. We instantiate it in the context of a recent state-of-the-art method and observe that it provides significant performance improvements on several domain adaptation benchmarks.Comment: NIPS 2018 accepted versio

Single and multiple instance learning for visual categorisation

University of Technology, Sydney. Faculty of Engineering and Information Technology.Nowadays, huge amounts of visual data, e.g., videos and images, have become widely accessible. Therefore, intelligently categorizing the large and growing collections of data for access convenience has been a central goal for modern computer vision research. In this thesis, we describe several newly-developed approaches for visual categorization upon the single and multiple instance learning cases. In single-instance learning (SIL), each of the training instances has been labeled. Here, we focus on a challenging task of facial expressions recognition where manually labeling each training instance, i.e., face video, is handy. To get the distinct features of expressions, we propose a novel feature representation, Histogram Variances Face (HVF), which integrates dynamic expression information into a static image being invariant to illumination and in-plane rotation. Through HVFs, the facial expression recognition can be cast as a facial recognition problem. We have applied our approach on the well-known Cohn-Kanade AU-Coded Facial Expression database, and then those extracted HVFs are classified by using facial recognition technology, i.e., Eigenfaces and Support Vector Machines (SVMs). The recognition accuracy is very encouraging. We further propose an extension of HVFs, Hexagonal Histogram Variance Faces (HHVFs), which applies HVFs on a hexagonal structure. Comparing to HVFs, HHVFs not only greatly reduce the computation costs but also improve the recognition accuracy. In multiple-instance learning (MIL), the training instances are divided into groups and the instances in the same group share only one label. MIL arises from many applications where individually labeling training instances is expensive. In this case, we propose a novel algorithm, multiple-instance learning with a supervised kernel density estimation (MIL-SKDE), to tackle the labeling ambiguity. Our algorithm extends the twin technologies, kernel density estimation (SKDE) and mean shift, to their supervised versions in which the labels of data points will affect the mode seeking. We apply MIL-SKDE in several applications of visual categorization, e.g., image and object categorization, and our algorithm performs superiorly comparing to other state-of-the-art methods. Furthermore, to address the complexity issue of MIL-SKDE, we propose MIL-SS (MIL with speed-up SKDE) to speed up the training process. Experiments shows that it has comparable performances to MIL-SKDE but is much more efficient in training stage. Finally, we apply MIL-SS in a “bag-of-words” (BoW) system to learn the visual codebook for object categorization on a more comprehensive dataset. Our system consists of four steps: codebook generation, feature coding, feature pooling and classification. Unlike conventional BoW methods that learn codebook from the whole image areas, our method can learn codebook just from the areas of target objects, which significantly improves classification accuracy

A survey on online active learning

Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in the context of online active learning. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research. Our review aims to provide a comprehensive and up-to-date overview of the field and to highlight directions for future work

Learning Independent Causal Mechanisms

Statistical learning relies upon data sampled from a distribution, and we usually do not care what actually generated it in the first place. From the point of view of causal modeling, the structure of each distribution is induced by physical mechanisms that give rise to dependences between observables. Mechanisms, however, can be meaningful autonomous modules of generative models that make sense beyond a particular entailed data distribution, lending themselves to transfer between problems. We develop an algorithm to recover a set of independent (inverse) mechanisms from a set of transformed data points. The approach is unsupervised and based on a set of experts that compete for data generated by the mechanisms, driving specialization. We analyze the proposed method in a series of experiments on image data. Each expert learns to map a subset of the transformed data back to a reference distribution. The learned mechanisms generalize to novel domains. We discuss implications for transfer learning and links to recent trends in generative modeling.Comment: ICML 201