/pdf/activation-functions-in-deep-learning-a-comprehensive-survey-11svx8wh.pdf

Activation functions in deep learning: A comprehensive survey and benchmark

Over the past decade, blockchain technology has attracted a huge attention from both industry and academia because it can be integrated with a large number of everyday applications of modern information and communication technologies (ICT). Peer-to-peer (P2P) architecture of blockchain enhances these applications by providing strong security and trust-oriented guarantees, such as immutability, verifiability, and decentralization. Despite these incredible features that blockchain technology brings to these ICT applications, recent research has indicated that the strong guarantees are not sufficient enough and blockchain networks may still be prone to various security, privacy, and reliability issues. In order to overcome these issues, it is important to identify the anomalous behaviour within the actionable time frame. In this article, we provide an in-depth survey regarding integration of anomaly detection models in blockchain technology. For this, we first discuss how anomaly detection can aid in ensuring security of blockchain based applications. Then, we demonstrate certain fundamental evaluation metrics and key requirements that can play a critical role while developing anomaly detection models for blockchain. Afterwards, we present a thorough survey of various anomaly detection models from the perspective of each layer of blockchain. Finally, we conclude the article by highlighting certain important challenges alongside discussing how they can serve as future research directions for new researchers in the field. 

/pdf/anomaly-detection-in-blockchain-networks-a-comprehensive-13mrw0xf.pdf

Anomaly Detection in Blockchain Networks: A Comprehensive Survey

Currently, deep learning-based visual inspection has been highly successful with the help of supervised learning methods. However, in real industrial scenarios, the scarcity of defect samples, the cost of annotation, and the lack of $a$ priori knowledge of defects may render supervised-based methods ineffective. In recent years, unsupervised anomaly localization (AL) algorithms have become more widely used in industrial inspection tasks. This article aims to help researchers in this field by comprehensively surveying recent achievements in unsupervised AL in industrial images using deep learning. The survey reviews more than 120 significant publications covering different aspects of AL, mainly covering various concepts, challenges, taxonomies, benchmark datasets, and quantitative performance comparisons of the methods reviewed. In reviewing the achievements to date, this article provides detailed predictions and analysis of several future research directions. This review provides detailed technical information for researchers interested in industrial AL and who wish to apply it to the localization of anomalies in other fields.

Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey

Over the past few years, anomaly detection, a subfield of machine learning that is mainly concerned with the detection of rare events, witnessed an immense improvement following the unprecedented growth of deep learning models. Recently, the emergence of self-supervised learning has sparked the development of new anomaly detection algorithms that surpassed state-of-the-art accuracy by a significant margin. This paper aims to review the current approaches in self-supervised anomaly detection. We present technical details of the common approaches and discuss their strengths and drawbacks. We also compare the performance of these models against each other and other state-of-the-art anomaly detection models. Finally, we discuss a variety of new directions for improving the existing algorithms.

Self-Supervised Anomaly Detection: A Survey and Outlook

Data augmentation is an important procedure in deep learning. GAN-based data augmentation can be utilized in many domains. For instance, in the credit card fraud domain, the imbalanced dataset problem is a major one as the number of credit card fraud cases is in the minority compared to legal payments. On the other hand, generative techniques are considered effective ways to rebalance the imbalanced class issue, as these techniques balance both minority and majority classes before the training. In a more recent period, Generative Adversarial Networks (GANs) are considered one of the most popular data generative techniques as they are used in big data settings. This research aims to present a survey on data augmentation using various GAN variants in the credit card fraud detection domain. In this survey, we offer a comprehensive summary of several peer-reviewed research papers on GAN synthetic generation techniques for fraud detection in the financial sector. In addition, this survey includes various solutions proposed by different researchers to balance imbalanced classes. In the end, this work concludes by pointing out the limitations of the most recent research articles and future research issues, and proposes solutions to address these problems.

/pdf/a-survey-on-gan-techniques-for-data-augmentation-to-address-3qkn1fu5.pdf

A Survey on GAN Techniques for Data Augmentation to Address the Imbalanced Data Issues in Credit Card Fraud Detection

GAN-based anomaly detection: A review

Semantic translation of face image with limited pixels for simulated prosthetic vision

ABSTRACT With the rapid development of UAV remote sensing, satellite remote sensing and computer vision, the semantic segmentation of remote sensing images has also developed rapidly and is widely used in research on land utilization classification, ecology, urban planning and other problems. Large differences in spatial and temporal scales, different image resolutions, insufficient model robustness to the data domain, and blurred object boundaries are the main problems for existing semantic segmentation models based on deep learning. This paper studies the problem of blurred target boundaries after semantic segmentation and propose a boundary enhancement loss function that highlights the importance of target edges. Compared to other models used in investigating higher boundary accuracy, the proposed model can be trained without boundary-labelled data, and no additional inference time is consumed. This loss function is applied to some other deep learning networks as a plug-and-play module on two different datasets and show that the IoU has an improvement of 2–5% with better clear boundary and continuity, which is more prominent on buildings and roads.

N. Ding

Papers

GAN-based anomaly detection: A review

Semantic translation of face image with limited pixels for simulated prosthetic vision

A boundary enhancement loss function for semantic segmentation of land cover