Noisy labeled data is more a norm than a rarity for self-generated content that is continuously published on the web and social media. Due to privacy concerns and governmental regulations, such a data stream can only be stored and used for learning purposes in a limited duration. To overcome the noise in this on-line scenario we propose QActor which novel combines: the selection of supposedly clean samples via quality models and actively querying an oracle for the most informative true labels. While the former can suffer from low data volumes of on-line scenarios, the latter is constrained by the availability and costs of human experts. QActor swiftly combines the merits of quality models for data filtering and oracle queries for cleaning the most informative data. The objective of QActor is to leverage the stringent oracle budget to robustly maximize the learning accuracy. QActor explores various strategies combining different query allocations and uncertainty measures. A central feature of QActor is to dynamically adjust the query limit according to the learning loss for each data batch. We extensively evaluate different image datasets fed into the classifier that can be standard machine learning (ML) models or deep neural networks (DNN) with noise label ratios ranging between 30% and 80%. Our results show that QActor can nearly match the optimal accuracy achieved using only clean data at the cost of at most an additional 6% of ground truth data from the oracle.
Continual learning from streaming data sources becomes more and more popular due to the increasing number of online tools and systems. Dealing with dynamic and everlasting problems poses new challenges for which traditional batch-based offline algorithms turn out to be insufficient in terms of computational time and predictive performance. One of the most crucial limitations is that we cannot assume having access to a finite and complete data set - we always have to be ready for new data that may complement our model. This poses a critical problem of providing labels for potentially unbounded …
Human activity recognition based on video streams has received numerous attentions in recent years. Due to lack of depth information, RGB video based activity recognition performs poorly compared to RGB-D video based solutions. On the other hand, acquiring depth information, inertia etc. is costly and requires special equipment, whereas RGB video streams are available in ordinary cameras. Hence, our goal is to investigate whether similar or even higher accuracy can be achieved with RGB-only modality. In this regard, we propose a novel framework that couples skeleton data extracted from RGB vid…
School of Biomedical Engineering, Medical School, Shenzhen University（深圳大学医学院生物医学工程学院）；Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging（广东省生物医学测量与超声成像重点实验室）；Institute of Computing and Intelligence, Harbin Institute of Technology（哈尔滨工业大学计算与智能研究院）；Peng Cheng Laboratory（鹏城实验室）；Shenzhen Mental Health Center, Shenzhen Kangning Hospital（深圳市精神卫生中心（深圳市康宁医院））
Learning from non-stationary data streams is a research direction that gains increasing interest as more data in form of streams becomes available, for example from social media, smartphones, or industrial process monitoring. Most approaches assume that the ground truth of the samples becomes available (possibly with some delay) and perform supervised online learning in the test-then-train scheme. While this assumption might be valid in some scenarios, it does not apply to all settings. In this work, we focus on scarcely labeled data streams and explore the potential of self-labeling in gradua…
Xerox Research Center Europe（施乐欧洲研究中心）；University of Trento（特伦托大学）
Learning object detectors requires massive amounts of labeled training samples from the specific data source of interest. This is impractical when dealing with many different sources (e.g., in camera networks), or constantly changing ones such as mobile cameras (e.g., in robotics or driving assistant systems). In this paper, we address the problem of self-learning detectors in an autonomous manner, i.e. (i) detectors continuously updating themselves to efficiently adapt to streaming data sources (contrary to transductive algorithms), (ii) without any labeled data strongly related to the target…
Number of connected devices is steadily increasing and these devices continuously generate data streams. Real-time processing of data streams is arousing interest despite many challenges. Clustering is one of the most suitable methods for real-time data stream processing, because it can be applied with less prior information about the data and it does not need labeled instances. However, data stream clustering differs from traditional clustering in many aspects and it has several challenging issues. Here, we provide information regarding the concepts and common characteristics of data streams,…
University of Central Florida（中佛罗里达大学）；University of Macau（澳门大学）；Nanjing University（南京大学）
In many scenarios, 1) data streams are generated in real time; 2) labeled data are expensive and only limited labels are available in the beginning; 3) real-world data is not always i.i.d. and data drift over time gradually; 4) the storage of historical streams is limited and model updating can only be achieved based on a very short lookback window. This learning setting limits the applicability and availability of many Machine Learning (ML) algorithms. We generalize the learning task under such setting as a semi-supervised drifted stream learning with short lookback problem (SDSL). SDSL impos…
University of Modena and Reggio Emilia（摩德纳-雷焦艾米利亚大学）
Continual Learning (CL) investigates how to train Deep Networks on a stream of tasks without incurring forgetting. CL settings proposed in literature assume that every incoming example is paired with ground-truth annotations. However, this clashes with many real-world applications: gathering labeled data, which is in itself tedious and expensive, becomes infeasible when data flow as a stream. This work explores Continual Semi-Supervised Learning (CSSL): here, only a small fraction of labeled input examples are shown to the learner. We assess how current CL methods (e.g.: EWC, LwF, iCaRL, ER, G…
Swedish Institute of Computer Science (SICS Swedish ICT)（瑞典计算机科学研究所（SICS瑞典信息与通信技术研究院））；Ludwig-Maximilians University（路德维希-马克西米利安大学）；Otto-von-Guericke University（奥托·冯·格里克大学）
Applications that learn from opinionated documents, like tweets or product reviews, face two challenges. First, the opinionated documents constitute an evolving stream, where both the author's attitude and the vocabulary itself may change. Second, labels of documents are scarce and labels of words are unreliable, because the sentiment of a word depends on the (unknown) context in the author's mind. Most of the research on mining over opinionated streams focuses on the first aspect of the problem, whereas for the second a continuous supply of labels from the stream is assumed. Such an assumptio…
Chonnam National University（全南国立大学）；University of Arkansas（阿肯色大学）；West Virginia University（西弗吉尼亚大学）
Electrocardiogram (ECG) signal is one of the most effective sources of information mainly employed for the diagnosis and prediction of cardiovascular diseases (CVDs) connected with the abnormalities in heart rhythm. Clearly, single modality ECG (i.e. time series) cannot convey its complete characteristics, thus, exploiting both time and time-frequency modalities in the form of time-series data and spectrogram is needed. Leveraging the cutting-edge self-supervised learning (SSL) technique on unlabeled data, we propose SSL-based multimodality ECG classification. Our proposed network follows SSL …
Santa Catarina State University（圣卡塔琳娜州立大学）；Pernambuco Federal University（伯南布哥联邦大学）；Granma University（格拉玛大学）
Classification algorithms to mine data stream have been extensively studied in recent years. However, a lot of these algorithms are designed for supervised learning which requires labeled instances. Nevertheless, the labeling of the data is costly and time-consuming. Because of this, alternative learning paradigms have been proposed to reduce the cost of the labeling process without significant loss of model performance. Active learning is one of these paradigms, whose main objective is to build classification models that request the lowest possible number of labeled examples achieving adequat…
Continuous learning from streaming data is among the most challenging topics in the contemporary machine learning. In this domain, learning algorithms must not only be able to handle massive volumes of rapidly arriving data, but also adapt themselves to potential emerging changes. The phenomenon of the evolving nature of data streams is known as concept drift. While there is a plethora of methods designed for detecting its occurrence, all of them assume that the drift is connected with underlying changes in the source of data. However, one must consider the possibility of a malicious injection…
Nanyang Technological University（南洋理工大学）；Singapore Institute of Manufacturing Technology（新加坡制造技术研究院）
We consider the problem of online unsupervised cross-domain adaptation, where two independent but related data streams with different feature spaces -- a fully labeled source stream and an unlabeled target stream -- are learned together. Unique characteristics and challenges such as covariate shift, asynchronous concept drifts, and contrasting data throughput arises. We propose ACDC, an adversarial unsupervised domain adaptation framework that handles multiple data streams with a complete self-evolving neural network structure that reacts to these defiances. ACDC encapsulates three modules int…
Warsaw University of Technology（华沙理工大学）；TECNALIA（泰克纳利亚）；University of the Basque Country (UPV/EHU)（巴斯克大学(UPV/EHU)）
Unsupervised anomaly discovery in stream data is a research topic with many practical applications. However, in many cases, it is not easy to collect enough training data with labeled anomalies for supervised learning of an anomaly detector in order to deploy it later for identification of real anomalies in streaming data. It is thus important to design anomalies detectors that can correctly detect anomalies without access to labeled training data. Our idea is to adapt the Online evolving Spiking Neural Network (OeSNN) classifier to the anomaly detection task. As a result, we offer an Online e…
Wrocław University of Science and Technology（弗罗茨瓦夫科技大学）
One of the significant problems of streaming data classification is the occurrence of concept drift, consisting of the change of probabilistic characteristics of the classification task. This phenomenon destabilizes the performance of the classification model and seriously degrades its quality. An appropriate strategy counteracting this phenomenon is required to adapt the classifier to the changing probabilistic characteristics. One of the significant problems in implementing such a solution is the access to data labels. It is usually costly, so to minimize the expenses related to this process…
Google Brain（谷歌大脑）；Google（谷歌）；UC Berkeley（加州大学伯克利分校）
We consider the task of semantic robotic grasping, in which a robot picks up an object of a user-specified class using only monocular images. Inspired by the two-stream hypothesis of visual reasoning, we present a semantic grasping framework that learns object detection, classification, and grasp planning in an end-to-end fashion. A "ventral stream" recognizes object class while a "dorsal stream" simultaneously interprets the geometric relationships necessary to execute successful grasps. We leverage the autonomous data collection capabilities of robots to obtain a large self-supervised datase…
Instituto Federal de Santa Catarina (IFSC)（圣卡塔琳娜联邦学院（IFSC））；Pontifícia Universidade Católica do Paraná (PUCPR)（巴拉那天主教大学（PUCPR））；École de Technologie Supérieure (ÉTS), Université du Québec（魁北克大学高等技术学院（ÉTS））；Universidade Estadual de Ponta Grossa (UEPG)（庞塔格罗萨州立大学（UEPG））
Toward Robust Semi-supervised Regression via Dual-stream Knowledge Distillation
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences（重庆绿色智能技术研究所，中国科学院）；Chongqing School, University of Chinese Academy of Sciences（中国科学院大学重庆学院）；Singapore Management University（新加坡管理大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
A deep clustering network is desired for data streams because of its aptitude in extracting natural features thus bypassing the laborious feature engineering step. While automatic construction of the deep networks in streaming environments remains an open issue, it is also hindered by the expensive labeling cost of data streams rendering the increasing demand for unsupervised approaches. This paper presents an unsupervised approach of deep clustering network construction on the fly via simultaneous deep learning and clustering termed Autonomous Deep Clustering Network (ADCN). It combines the f…
Western Washington University（西华盛顿大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）
Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time. Our models decompose anomaly scores into the contributions of individual user behavior features for increased interpretability …
NALBI Inc.（NALBI公司）；Seoul National University（首尔大学）
Continually learning in the real world must overcome many challenges, among which noisy labels are a common and inevitable issue. In this work, we present a repla-ybased continual learning framework that simultaneously addresses both catastrophic forgetting and noisy labels for the first time. Our solution is based on two observations; (i) forgetting can be mitigated even with noisy labels via self-supervised learning, and (ii) the purity of the replay buffer is crucial. Building on this regard, we propose two key components of our method: (i) a self-supervised replay technique named Self-Repl…
We study Online Continual Learning with missing labels and propose SemiCon, a new contrastive loss designed for partly labeled data. We demonstrate its efficiency by devising a memory-based method trained on an unlabeled data stream, where every data added to memory is labeled using an oracle. Our approach outperforms existing semi-supervised methods when few labels are available, and obtain similar results to state-of-the-art supervised methods while using only 2.6% of labels on Split-CIFAR10 and 10% of labels on Split-CIFAR100.
Labeling data correctly is an expensive and challenging task in machine learning, especially for on-line data streams. Deep learning models especially require a large number of clean labeled data that is very difficult to acquire in real-world problems. Choosing useful data samples to label while minimizing the cost of labeling is crucial to maintain efficiency in the training process. When confronted with multiple labelers with different expertise and respective labeling costs, deciding which labeler to choose is nontrivial. In this paper, we consider a novel weak and strong labeler problem i…
Google uses continuous streams of data from industry partners in order to deliver accurate results to users. Unexpected drops in traffic can be an indication of an underlying issue and may be an early warning that remedial action may be necessary. Detecting such drops is non-trivial because streams are variable and noisy, with roughly regular spikes (in many different shapes) in traffic data. We investigated the question of whether or not we can predict anomalies in these data streams. Our goal is to utilize Machine Learning and statistical approaches to classify anomalous drops in periodic, b…
We propose self-training with noisy student-teacher approach for streaming keyword spotting, that can utilize large-scale unlabeled data and aggressive data augmentation. The proposed method applies aggressive data augmentation (spectral augmentation) on the input of both student and teacher and utilize unlabeled data at scale, which significantly boosts the accuracy of student against challenging conditions. Such aggressive augmentation usually degrades model performance when used with supervised training with hard-labeled data. Experiments show that aggressive spec augmentation on baseline s…
Detecting when eating occurs is an essential step toward automatic dietary monitoring, medication adherence assessment, and diet-related health interventions. Wearable technologies play a central role in designing unubtrusive diet monitoring solutions by leveraging machine learning algorithms that work on time-series sensor data to detect eating moments. While much research has been done on developing activity recognition and eating moment detection algorithms, the performance of the detection algorithms drops substantially when the model trained with one user is utilized by a new user. To fac…
University of Calabria（卡塔尼娅大学）；ICAR-CNR（ICAR-国家研究委员会）；Revelis s.r.l.（Revelis公司）；Artificial Intelligence Institute, University of Waikato, NZ（瓦卡托大学人工智能研究所）；LTCI, Télécom Paris, Institut Polytechnique de Paris（LTCI，巴黎电信，巴黎理工学院）
University of Pittsburgh（匹兹堡大学）；Univesity of Texas - Rio Grande Valley（得克萨斯大学里奥格兰德谷分校）；U.S. Geological Survey（美国地质调查局）
Effective training of advanced ML models requires large amounts of labeled data, which is often scarce in scientific problems given the substantial human labor and material cost to collect labeled data. This poses a challenge on determining when and where we should deploy measuring instruments (e.g., in-situ sensors) to collect labeled data efficiently. This problem differs from traditional pool-based active learning settings in that the labeling decisions have to be made immediately after we observe the input data that come in a time series. In this paper, we develop a real-time active learni…
University of North Carolina Charlotte（北卡罗来纳大学夏洛特分校）
The effect of bias on hypothesis formation is characterized for an automated data-driven projection pursuit neural network to extract and select features for binary classification of data streams. This intelligent exploratory process partitions a complete vector state space into disjoint subspaces to create working hypotheses quantified by similarities and differences observed between two groups of labeled data streams. Data streams are typically time sequenced, and may exhibit complex spatio-temporal patterns. For example, given atomic trajectories from molecular dynamics simulation, the mach…
All Eyes on the Workflow: Automated and Efficient Event Discovery from Video Streams
Chair of Process and Data Science, RWTH Aachen University（过程与数据科学教授席位，亚琛工业大学）；Artificial Intelligence & Machine Learning Lab, Technical University of Darmstadt（人工智能与机器学习实验室，达姆施塔特技术大学）
Qatar Computing Research Institute – HBKU（卡塔尔计算研究所 – 哈马德·本·哈利法大学）
The role of social media, in particular microblogging platforms such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly acknowledged. However, time-critical analysis of big crisis data on social media streams brings challenges to machine learning techniques, especially the ones that use supervised learning. The Scarcity of labeled data, particularly in the early hours of a crisis, delays the machine learning process. The current state-of-the-art classification methods require a significant amount of labeled data specific to a particular event for …
FiLark: a streaming-first software framework for end-to-end exploration, annotation, and algorithm integration in distributed acoustic sensing
organization= State Key Laboratory of Ocean Sensing \& Ocean College, Zhejiang University , city= Zhoushan , postcode= 316021 , country= China；organization= College of Information Science；Electronic Engineering, Zhejiang University , city= Hangzhou , country= China；organization= College of Computer Science；Technology, Zhejiang University , city= Hangzhou , country= China
With the advent of deep learning for computer vision tasks, the need for accurately labeled data in large volumes is vital for any application. The increasingly available large amounts of solar image data generated by the Solar Dynamic Observatory (SDO) mission make this domain particularly interesting for the development and testing of deep learning systems. The currently available labeled solar data is generated by the SDO mission's Feature Finding Team's (FFT) specialized detection modules. The major drawback of these modules is that detection and labeling is performed with a cadence of eve…
Huawei Technologies Israel Research Center（华为技术以色列研究中心）
In stream-based active learning, the learning procedure typically has access to a stream of unlabeled data instances and must decide for each instance whether to label it and use it for training or to discard it. There are numerous active learning strategies which try to minimize the number of labeled samples required for training in this setting by identifying and retaining the most informative data samples. Most of these schemes are rule-based and rely on the notion of uncertainty, which captures how small the distance of a data sample is from the classifier's decision boundary. Recently, th…
University of Windsor（温莎大学）；University of New Brunswick（新不伦瑞克大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；New York Power Authority（纽约电力局）
This paper presents a novel data-driven framework to aid in system state estimation when the power system is under unobservable false data injection attacks. The proposed framework dynamically detects and classifies false data injection attacks. Then, it retrieves the control signal using the acquired information. This process is accomplished in three main modules, with novel designs, for detection, classification, and control signal retrieval. The detection module monitors historical changes in phasor measurements and captures any deviation pattern caused by an attack on a complex plane. This…
Accurate and Data-Efficient Micro-XRD Phase Identification Using Multi-Task Learning: Application to Hydrothermal Fluids
Pacific Northwest National Laboratory（太平洋西北国家实验室）；Washington State University（华盛顿州立大学）；Argonne National Laboratory（阿贡国家实验室）；University of Rochester（罗切斯特大学）
UT-Battelle, LLC（UT-Battelle有限责任公司）；U.S. Department of Energy（美国能源部）
The proliferation of optical, electron, and scanning probe microscopies gives rise to large volumes of imaging data of objects as diversified as cells, bacteria, pollen, to nanoparticles and atoms and molecules. In most cases, the experimental data streams contain images having arbitrary rotations and translations within the image. At the same time, for many cases, small amounts of labeled data are available in the form of prior published results, image collections, and catalogs, or even theoretical models. Here we develop an approach that allows generalizing from a small subset of labeled dat…
