ATL: Autonomous Knowledge Transfer from Many Streaming Processes

Transferring knowledge across many streaming processes remains an uncharted territory in the existing literature and features unique characteristics: no labelled instance of the target domain, covariate shift of source and target domain, different period of drifts in the source and target domains. Autonomous transfer learning (ATL) is proposed in this paper as a flexible deep learning approach for the online unsupervised transfer learning problem across many streaming processes. ATL offers an online domain adaptation strategy via the generative and discriminative phases coupled with the KL divergence based optimization strategy to produce a domain invariant network while putting forward an elastic network structure. It automatically evolves its network structure from scratch with/without the presence of ground truth to overcome independent concept drifts in the source and target domain. The rigorous numerical evaluation has been conducted along with a comparison against recently published works. ATL demonstrates improved performance while showing significantly faster training speed than its counterparts.

Cross-domain Network Representations

The purpose of network representation is to learn a set of latent features by obtaining community information from network structures to provide knowledge for machine learning tasks. Recent research has driven significant progress in network representation by employing random walks as the network sampling strategy. Nevertheless, existing approaches rely on domain-specifically rich community structures and fail in the network that lack topological information in its own domain. In this paper, we propose a novel algorithm for cross-domain network representation, named as CDNR. By generating the random walks from a structural rich domain and transferring the knowledge on the random walks across domains, it enables a network representation for the structural scarce domain as well. To be specific, CDNR is realized by a cross-domain two-layer node-scale balance algorithm and a cross-domain two-layer knowledge transfer algorithm in the framework of cross-domain two-layer random walk learning. Experiments on various real-world datasets demonstrate the effectiveness of CDNR for universal networks in an unsupervised way.

Open Set Domain Adaptation: Theoretical Bound and Algorithm

Unsupervised domain adaptation for classification tasks has achieved great progress in leveraging the knowledge in a labeled (source) domain to improve the task performance in an unlabeled (target) domain by mitigating the effect of distribution discrepancy. However, most existing methods can only handle unsupervised closed set domain adaptation (UCSDA), where the source and target domains share the same label set. In this paper, we target a more challenging but realistic setting: unsupervised open set domain adaptation (UOSDA), where the target domain has unknown classes that the source domain does not have. This study is the first to give the generalization bound of open set domain adaptation through theoretically investigating the risk of the target classifier on the unknown classes. The proposed generalization bound for open set domain adaptation has a special term, namely open set difference, which reflects the risk of the target classifier on unknown classes. According to this generalization bound, we propose a novel and theoretically guided unsupervised open set domain adaptation method: Distribution Alignment with Open Difference (DAOD), which is based on the structural risk minimization principle and open set difference regularization. The experiments on several benchmark datasets show the superior performance of the proposed UOSDA method compared with the state-of-the-art methods in the literature.

Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation

In unsupervised domain adaptation (UDA), classifiers for the target domain (TD) are trained with clean labeled data from the source domain (SD) and unlabeled data from TD. However, in the wild, it is hard to acquire a large amount of perfectly clean labeled data in SD given limited budget. Hence, we consider a new, more realistic and more challenging problem setting, where classifiers have to be trained with noisy labeled data from SD and unlabeled data from TD---we name it wildly UDA (WUDA). We show that WUDA provably ruins all UDA methods if taking no care of label noise in SD, and to this end, we propose a Butterfly framework, a panacea for all difficulties in WUDA. Butterfly maintains four models (e.g., deep networks) simultaneously, where two take care of all adaptations (i.e., noisy-to-clean, labeled-to-unlabeled, and SD-to-TD-distributional) and then the other two can focus on classification in TD. As a consequence, Butterfly possesses all the necessary components for all the challenges in WUDA. Experiments demonstrate that under WUDA, Butterfly significantly outperforms existing baseline methods.

Butterfly: Robust One-step Approach towards Wildly-unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) trains with clean labeled data in source domain and unlabeled data in target domain to classify target-domain data. However, in real-world scenarios, it is hard to acquire fully-clean labeled data in source domain due to the expensive labeling cost. This brings us a new but practical adaptation called wildly-unsupervised domain adaptation (WUDA), which aims to transfer knowledge from noisy labeled data in source domain to unlabeled data in target domain. To tackle the WUDA, we present a robust one-step approach called Butterfly, which trains four networks. Specifically, two networks are jointly trained on noisy labeled data in source domain and pseudo-labeled data in target domain (i.e., data in mixture domain). Meanwhile, the other two networks are trained on pseudo-labeled data in target domain. By using dual-checking principle, Butterfly can obtain high-quality target-specific representations. We conduct experiments to demonstrate that Butterfly significantly outperforms other baselines on simulated and real-world WUDA tasks in most cases.

A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis

Mobile app development in recent years has resulted in new products and features to improve human life. Mobile telematics is one such development that encompasses multidisciplinary fields for transportation safety. The application of mobile telematics has been explored in many areas, such as insurance and road safety. However, to the best of our knowledge, its application in gender detection has not been explored. This paper proposes a Choquet fuzzy integral vertical bagging classifier that detects gender through mobile telematics. In this model, different random forest classifiers are trained by randomly generated features with rough set theory, and the top three classifiers are fused using the Choquet fuzzy integral. The model is implemented and evaluated on a real dataset. The empirical results indicate that the Choquet fuzzy integral vertical bagging classifier outperforms other classifiers.

Deep Uncertainty Learning: A Machine Learning Approach for Weather Forecasting

This paper uses the weather forecasting as an application background to illustrate the technique of \textit{deep uncertainty learning} (DUL). Weather forecasting has great significance throughout human history and is traditionally approached through numerical weather prediction (NWP) in which the atmosphere is modelled as differential equations. However, due to the instability of these differential equations in the presence of uncertainties, weather forecasting through numerical simulations may not be reliable. This paper explores weather forecasting as a data mining problem. We build a deep prediction interval (DPI) model based on sequence-to-sequence (seq2seq) that predicts spatio-temporal patterns of meteorological variables in the future 37 hours, which incorporates the informative knowledge of NWP. A big contribution and surprising finding in the training process of DPI is that training by mean variance error (MVE) loss instead of mean square error loss can significantly improve the generalization of point estimation, which has never been reported in previous researches. We think this phenomenon can be regarded as a new kind of regularization which can not only be on a par with the famous Dropout but also provide more uncertainty information, and hence comes into win-win situation. Based on single DPI, we then build deep ensemble. We evaluate our method on dataset from 10 realistic weather stations in Beijing of China. Experimental results shown DPI has better generalization than traditional point estimation and deep ensemble can further improve the performance. The deep ensemble method also achieved top-2 online score ranking in the competition of AI Challenger 2018. It can dramatically decrease up to 56\% error compared with NWP.

Deep Surface Light Fields

A surface light field represents the radiance of rays originating from any points on the surface in any directions. Traditional approaches require ultra-dense sampling to ensure the rendering quality. In this paper, we present a novel neural network based technique called deep surface light field or DSLF to use only moderate sampling for high fidelity rendering. DSLF automatically fills in the missing data by leveraging different sampling patterns across the vertices and at the same time eliminates redundancies due to the network's prediction capability. For real data, we address the image registration problem as well as conduct texture-aware remeshing for aligning texture edges with vertices to avoid blurring. Comprehensive experiments show that DSLF can further achieve high data compression ratio while facilitating real-time rendering on the GPU.

Heterogeneous Transfer Learning: An Unsupervised Approach

Transfer learning leverages the knowledge in one domain, the source domain, to improve learning efficiency in another domain, the target domain. Existing transfer learning research is relatively well-progressed, but only in situations where the feature spaces of the domains are homogeneous and the target domain contains at least a few labeled instances. However, transfer learning has not been well-studied in heterogeneous settings with an unlabeled target domain. To contribute to the research in this emerging field, this paper presents: (1) an unsupervised knowledge transfer theorem that prevents negative transfer; and (2) a principal angle-based metric to measure the distance between two pairs of domains. The metric shows the extent to which homogeneous representations have preserved the information in original source and target domains. The unsupervised knowledge transfer theorem sets out the transfer conditions necessary to prevent negative transfer. Linear monotonic maps meet the transfer conditions of the theorem and, hence, are used to construct homogeneous representations of the heterogeneous domains, which in principle prevents negative transfer. The metric and the theorem have been implemented in an innovative transfer model, called a Grassmann-LMM-geodesic flow kernel (GLG), that is specifically designed for knowledge transfer across heterogeneous domains. The GLG model learns homogeneous representations of heterogeneous domains by minimizing the proposed metric. Knowledge is transferred through these learned representations via a geodesic flow kernel. Notably, the theorem presented in this paper provides the sufficient transfer conditions needed to guarantee that knowledge is transferred from a source domain to an unlabeled target domain with correctness.

Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55

We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation of 3D shapes and 3D reconstruction from single view images. Ten teams have participated in the challenge and the best performing teams have outperformed state-of-the-art approaches on both tasks. A few novel deep learning architectures have been proposed on various 3D representations on both tasks. We report the techniques used by each team and the corresponding performances. In addition, we summarize the major discoveries from the reported results and possible trends for the future work in the field.