Cross-Domain Similarity Learning for Face Recognition in Unseen Domains

/in /by

Face recognition models trained under the assumption of identical training and test distributions often suffer from poor generalization when faced with unknown variations, such as a novel ethnicity or unpredictable individual make-ups during test time. In this paper, we introduce a novel cross-domain metric learning loss, which we dub Cross-Domain Triplet (CDT) loss, to improve face recognition in unseen domains. The CDT loss encourages learning semantically meaningful features by enforcing compact feature clusters of identities from one domain, where the compactness is measured by underlying similarity metrics that belong to another training domain with different statistics. Intuitively, it discriminatively correlates explicit metrics derived from one domain, with triplet samples from another domain in a unified loss function to be minimized within a network, which leads to better alignment of the training domains. The network parameters are further enforced to learn generalized features under domain shift, in a model-agnostic learning pipeline. Unlike the recent work of Meta Face Recognition [18], our method does not require careful hard-pair sample mining and filtering strategy during training. Extensive experiments on various face recognition benchmarks show the superiority of our method in handling variations, compared to baseline and the state-of-the-art methods.

Read more

Automated Anomaly Detection via Curiosity-Guided Search and Self-Imitation Learning

/in /by

Anomaly detection is an important data mining task with numerous applications, such as intrusion detection, credit card fraud detection, and video surveillance. However, given a specific complicated task with complicated data, the process of building an effective deep learning-based system for anomaly detection still highly relies on human expertise and laboring trials. Also, while neural architecture search (NAS) has shown its promise in discovering effective deep architectures in various domains, such as image classification, object detection, and semantic segmentation, contemporary NAS methods are not suitable for anomaly detection due to the lack of intrinsic search space, unstable search process, and low sample efficiency. To bridge the gap, in this article, we propose AutoAD, an automated anomaly detection framework, which aims to search for an optimal neural network model within a predefined search space. Specifically, we first design a curiosity-guided search strategy to overcome the curse of local optimality. A controller, which acts as a search agent, is encouraged to take actions to maximize the information gain about the controller’s internal belief. We further introduce an experience replay mechanism based on self-imitation learning to improve the sample efficiency. Experimental results on various real-world benchmark datasets demonstrate that the deep model identified by AutoAD achieves the best performance, comparing with existing handcrafted models and traditional search methods.

Read more

Survivable Distributed Fiber Optic Sensors Placement against Single Link Failure

/in /by

Empowered by the rapid advancement of fiber optic sensing techniques in recent years, network carriers are able to upgrade their network infrastructure beyond the basic communication services with extra sensing applications and services (e.g., monitoring traffic and road condition, leakage detection, etc.), thus evolving to a new era of Infrastructure-as-a-Sensor (IaaSr) or Network-as-a-Sensor (NaaSr). When network carriers upgrade their network infrastructures with distributed fiber optic sensing (DFOS) technique to provide IaaSr services, there will arise a critical challenge: how to provide survivable (or reliable) IaaSr services against network failures (e.g., fiber cut). In this work, for the first time, we investigate the problem of survivable DFOS placement against single link failure. More specifically, we study where to place the primary and backup sensors and how to assign the primary and backup fiber sensing routes, with the objective of minimizing the number of sensors used. We formulate the problem using Integer Linear Programming (ILP) to facilitate the optimal solution. In addition, we propose a set of efficient heuristic algorithms to solve the problem in a fast manner. In particular, the proposed Shared-one algorithm provides a cost-efficient shared protection, through a one-step global optimization of the assignment of primary and backup DFOS placement. We conduct extensive simulations to evaluate the performance of the proposed solutions. We find out that Shared-one can achieve a close-to-optimal performance, compared to the ILP optimal results, while outperforming the other heuristic solutions with an average performance improvement by at least 16%.

Read more

Unsupervised Concept Representation Learning for Length-Varying Text Similarity

/in /by

Measuring document similarity plays an important role in natural language processing tasks. Most existing document similarity approaches suffer from the information gap caused by context and vocabulary mismatches when comparing varying-length texts. In this paper, we propose an unsupervised concept representation learning approach to address the above issues. Specifically, we propose a novel Concept Generation Network (CGNet) to learn concept representations from the perspective of the entire text corpus. Moreover, a concept-based document matching method is proposed to leverage advances in the recognition of local phrase features and corpus-level concept features. Extensive experiments on real-world data sets demonstrate that new method can achieve a considerable improvement in comparing length-varying texts. In particular, our model achieved 6.5% better F1 Score compared to the best of the baseline models for a concept-project benchmark dataset.

Read more

Vehicle Run-Off-Road Event Automatic Detection by Fiber Sensing Technology

/in /by

We demonstrate a new application of fiber-optic-sensing and machine learning techniques for vehicle run-off-road events detection to enhance roadway safety and efficiency. The proposed approach achieves high accuracy in a testbed under various experimental conditions.

Read more

Static Weight Detection and Localization on Aerial Fiber Cables using Distributed Acoustic Sensing

/in /by

We demonstrated for the first time to our knowledge, the detection and localization of a static weight on an aerial cable by using frequency domain decomposition analysis of ambient vibrations detected by a φ-DAS system.

Read more

Nonlinear Impairment Compensation using Neural Networks

/in /by

Neural networks are attractive for nonlinear impairment compensation applications in communication systems. In this paper, several approaches to reduce computational complexity of the neural network-based algorithms are presented.

Read more

Field Trial of Abnormal Activity Detection and Threat Level Assessment with Fiber Optic Sensing for Telecom Infrastructure Protection

/in /by

We report the field trial results of monitoring abnormal activities near deployed cable with fiber-optic-sensing technology for cable protection. Detection and position determination of abnormal events and evaluating the threat to the cable is realized.

Read more

Estimation of Core-Cladding Concentricity Error From GAWBS Noise Spectrum

/in /by

CCCE in a 60-km fiber is estimated from its GAWBS noise spectrum by comparing the TR 1m modes with the R 0m modes. The estimated CCCE value 0.73 μm is consistent with conventional measurements of 0.6–0.8 μm.

Read more

Field Trial of Vibration Detection and Localization using Coherent Telecom Transponders over 380-km Link

/in /by

We demonstrate vibration detection and localization based on extracting optical phase from the DSP elements of a coherent receiver in bidirectional WDM transmission of 200-Gb/s DP-16QAM over 380 km of installed field fiber.

Read more