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Deep learning (DL)-based specific emitter identification (SEI) technique can automatically extract radio frequency (RF) fingerprint features in RF signals to distinguish between legal and illegal devices and enhance the security of wireless network. However, deep neural network (DNN) can easily be fooled by adversarial examples or perturbations of the input data. If a malicious device emits signals containing a specially designed adversarial samples, will the DL-based SEI still work stably to correctly identify the malicious device? To the best of our knowledge, this research is still blank, let alone the corresponding defense methods. Therefore, this paper designs two scenarios of attack and defense and proposes the corresponding implementation methods to specializes in the robustness of DL-based SEI under adversarial attacks. On this basis, detailed experiments are carried out based on the real-world data and simulation data. The attack scenario is that the malicious device adds an adversarial perturbation signal specially designed to the original signal, misleading the original system to make a misjudgment. Experiments based on three different attack generation methods show that DL-based SEI is very vulnerability. Even if the intensity is very low, without affecting the probability density distribution of the original signal, the performance can be reduced to about 50%, and at −22 dB it is completely invalid. In the defense scenario, the adversarial training (AT) of DL-based SEI is added, which can significantly improve the system's performance under adversarial attacks, with ≥60% improvement in the recognition rate compared to the network without AT. Further, AT has a more robust effect on white noise. This study fills the relevant gaps and provides guidance for future research. In the future research, the impact of adversarial attacks must be considered, and it is necessary to add adversarial training in the training process. [ABSTRACT FROM AUTHOR]

For image classification in computer vision, the performance of conventional deep neural networks (DNN) may usually drop when labeled training samples are limited. In this case, few-shot learning (FSL) or particularly zero-shot learning (ZSL), i.e. classification of target classes with few or zero labeled training samples, was proposed to imitate the strong learning ability of human. However, recent investigations show that most existing ZSL models may easily overfit and they tend to misclassify the target instance as one class seen in the training set. To alleviate this problem, we proposed an embedding based ZSL method with a self-focus mechanism, i.e. a focus-ratio that introduces the importance of each dimension, into the model optimization process. The objective function will be reconstructed according to these focus-ratios encouraging that the embedding model focus exclusively on important dimensions in the target space. As the self-focus module only takes part in the training process, the over-fitting knowledge is apportioned, and hence the rest embedding model can become more generalized for the new classes during test. Experimental results on four benchmarks, including AwA1, AwA2, aPY and CUB, show that our method outperforms the state-of-the-art methods on coarse-grained ZSL tasks while not affecting the performance of fine-grained ZSL. Additionally, several comparisons demonstrate the superiority of the proposed mechanism. [ABSTRACT FROM AUTHOR]

Dimensionality Reduction (DR) is a fundamental topic of pattern classification and machine learning. For classification tasks, DR is typically employed as a pre-processing step, succeeded by an independent classifier training stage. However, such independent operation of the two stages often limits the final classification performance notably, as the generated subspace may not be maximally beneficial or appropriate to the learning task at hand. This problem is further accentuated for high-dimensional data classification in situations of the limited number of samples. To address this problem, we develop a novel joint learning model for classification, referred to as two-layer mixture of factor analyzers with joint factor loading (2L-MJFA). Specifically, the model adopts a special two-layer mixture or a mixture of mixtures structure, where each component represents each specific class as a mixture of factor analyzers (MFA). Importantly, all the involved factor analyzers are intentionally designed so that they share the same loading matrix. This, apart from operating as the DR matrix, largely reduces the parameters and makes the proposed algorithm very suitable to small dataset situations. Additionally, we propose a modified expectation maximization algorithm to train the proposed model. A series of simulation experiments demonstrate that what we propose significantly outperforms other state-of-the-art algorithms on various benchmark datasets. Finally, since factor analyzers are closely linked with Auto-encoder networks, the proposed idea could be of particular utility to the community of neural networks. [ABSTRACT FROM AUTHOR]

• We present a timely and comprehensive survey on robust adversarial training. • This survey offers the fundamentals of adversarial training, a unified theory that can be used to interpret various methods, and a comprehensive summarization of different methodologies. • This survey also addresses three important research focus in adversarial training: interpretability, robust generalization, and robustness evaluation, which can stimulate future inspirations as well as research outlook. Deep neural networks have achieved remarkable success in machine learning, computer vision, and pattern recognition in the last few decades. Recent studies, however, show that neural networks (both shallow and deep) may be easily fooled by certain imperceptibly perturbed input samples called adversarial examples. Such security vulnerability has resulted in a large body of research in recent years because real-world threats could be introduced due to the vast applications of neural networks. To address the robustness issue to adversarial examples particularly in pattern recognition, robust adversarial training has become one mainstream. Various ideas, methods, and applications have boomed in the field. Yet, a deep understanding of adversarial training including characteristics, interpretations, theories, and connections among different models has remained elusive. This paper presents a comprehensive survey trying to offer a systematic and structured investigation on robust adversarial training in pattern recognition. We start with fundamentals including definition, notations, and properties of adversarial examples. We then introduce a general theoretical framework with gradient regularization for defending against adversarial samples - robust adversarial training with visualizations and interpretations on why adversarial training can lead to model robustness. Connections will also be established between adversarial training and other traditional learning theories. After that, we summarize, review, and discuss various methodologies with defense/training algorithms in a structured way. Finally, we present analysis, outlook, and remarks on adversarial training. [ABSTRACT FROM AUTHOR]

Semi-supervised learning (SSL) is a typical learning paradigms training a model from both labeled and unlabeled data. The traditional SSL models usually assume unlabeled data are relevant to the labeled data, i.e., following the same distributions of the targeted labeled data. In this paper, we address a different, yet formidable scenario in semi-supervised classification, where the unlabeled data may contain irrelevant data to the labeled data. To tackle this problem, we develop a maximum margin model, named tri-class support vector machine (3C-SVM), to utilize the available training data, while seeking a hyperplane for separating the targeted data well. Our 3C-SVM exhibits several characteristics and advantages. First, it does not need any prior knowledge and explicit assumption on the data relatedness. On the contrary, it can relieve the effect of irrelevant unlabeled data based on the logistic principle and maximum entropy principle. That is, 3C-SVM approaches an ideal classifier. This classifier relies heavily on labeled data and is confident on the relevant data lying far away from the decision hyperplane, while maximally ignoring the irrelevant data, which are hardly distinguished. Second, theoretical analysis is provided to prove that in what condition, the irrelevant data can help to seek the hyperplane. Third, 3C-SVM is a generalized model that unifies several popular maximum margin models, including standard SVMs, Semi-supervised SVMs (S 3 VMs), and SVMs learned from the universum ( U -SVMs) as its special cases. More importantly, we deploy a concave–convex produce to solve the proposed 3C-SVM, transforming the original mixed integer programming, to a semi-definite programming relaxation, and finally to a sequence of quadratic programming subproblems, which yields the same worst case time complexity as that of S 3 VMs. Finally, we demonstrate the effectiveness and efficiency of our proposed 3C-SVM through systematical experimental comparisons. [ABSTRACT FROM AUTHOR]

Whilst adversarial training has been proven to be one most effective defending method against adversarial attacks for deep neural networks, it suffers from over-fitting on training adversarial data and thus may not guarantee the robust generalization. This may result from the fact that the conventional adversarial training methods generate adversarial perturbations usually in a supervised way so that the resulting adversarial examples are highly biased towards the decision boundary, leading to an inhomogeneous data distribution. To mitigate this limitation, we propose to generate adversarial examples from a perturbation diversity perspective. Specifically, the generated perturbed samples are not only adversarial but also diverse so as to certify robust generalization and significant robustness improvement through a homogeneous data distribution. We provide theoretical and empirical analysis, establishing a foundation to support the proposed method. As a major contribution, we prove that promoting perturbations diversity can lead to a better robust generalization bound. To verify our methods' effectiveness, we conduct extensive experiments over different datasets (e.g., CIFAR-10, CIFAR-100, SVHN) with different adversarial attacks (e.g., PGD, CW). Experimental results show that our method outperforms other state-of-the-art (e.g., PGD and Feature Scattering) in robust generalization performance. • Improved adversarial robustness from a diversity perspective. • Showed that robust generalization can be upper bounded by a diversity term. • Achieved competitive performace against adversarial examples. [ABSTRACT FROM AUTHOR]

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. • We categorize brain age estimation models based on the deep learning architectures. • We classify deep learning architectures based on the slice/voxel-based input data. • We examine the modalities, size of data, performance and the datasets used. • We enumerate several challenges and trace directions that can be explored further. [ABSTRACT FROM AUTHOR]

Knowledge resources, e.g. knowledge graphs, which formally represent essential semantics and information for logic inference and reasoning, can compensate for the unawareness nature of many natural language processing techniques based on deep neural networks. This paper provides a focused review of the emerging but intriguing topic that fuses quality external knowledge resources in improving the performance of natural language processing tasks. Existing methods and techniques are summarised in three main categories: (1) static word embeddings, (2) sentence-level deep learning models, and (3) contextualised language representation models, depending on when, how and where external knowledge is fused into the underlying learning models. We focus on the solutions to mitigate two issues: knowledge inclusion and inconsistency between language and knowledge. Details on the design of each representative method, as well as their strength and limitation, are discussed. We also point out some potential future directions in view of the latest trends in natural language processing research. • First comprehensive review on knowledge fusion into deep learning for NLP. • A taxonomy for the related research and extensive overview of the state-of-the-art. • A focus on the theoretical formulation and optimisation methods. • Harmonise the mathematical notations across a plethora of publications. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNN), such as convolutional neural networks (CNN) have been widely used for object recognition. However, they are usually unable to ensure the required intra-class compactness and inter-class separability in the kernel space. These are known to be important in pattern recognition for achieving both robustness and accuracy. In this paper, we propose to integrate a kernelized Min-Max objective in the DNN training in order to explicitly enforce both kernelized within-class compactness and between-class margin. The involved kernel space is implicitly mapped from the feature space associated with a certain upper layer of DNN by exploiting a kernel trick, while the Min-Max objective in this space is interpolated with the original DNN loss function and finally optimized in the training phase. With a very small additional computation cost, the proposed strategy can be easily integrated in different DNN models without changing any other part of the original model. The comparative recognition accuracy of the proposed method is evaluated with multiple DNN models (including shallow CNN, deep CNN and deep residual neural network models) on two benchmark datasets: CIFAR-10 and CIFAR-100. Extensive experimental results demonstrate that the integration of kernelized Min-Max objective in the training of DNN models can achieve better results compared to state-of-the-art models, without incurring additional model complexity. [ABSTRACT FROM AUTHOR]

• We extend the successful RPN+BF framework to combine handcrafted features and CNN features for pedestrian detection. • RoI-pooling is used to extract features from handcrafted and CNN channels, and with larger output resolution for the former. • We explore several handcrafted channels such as HOG+LUV, Checkerboards, and RotatedFilters in our framework. • Experimental results on Caltech pedestrian dataset demonstrate the effectiveness of the proposed framework. • When using a more advanced RPN, our approach can be further improved and get competitive results on the benchmarks. Pedestrian detection has achieved great improvements with the help of Convolutional Neural Networks (CNNs). CNN can learn high-level features from input images, but the insufficient spatial resolution of CNN feature channels (feature maps) may cause a loss of information, which is harmful especially to small instances. In this paper, we propose a new pedestrian detection framework, which extends the successful RPN+BF framework to combine handcrafted features and CNN features. RoI-pooling is used to extract features from both handcrafted channels (e.g. HOG+LUV, CheckerBoards or RotatedFilters) and CNN channels. Since handcrafted channels always have higher spatial resolution than CNN channels, we apply RoI-pooling with larger output resolution to handcrafted channels to keep more detailed information. Our ablation experiments show that the developed handcrafted features can reach better detection accuracy than the CNN features extracted from the VGG-16 net, and a performance gain can be achieved by combining them. Experimental results on Caltech pedestrian dataset with the original annotations and the improved annotations demonstrate the effectiveness of the proposed approach. When using a more advanced RPN in our framework, our approach can be further improved and get competitive results on both benchmarks. [ABSTRACT FROM AUTHOR]

• Multi-semantic hypergraph explore higher-order relationships among few samples. • Orthogonalized mapping function helps to obtain rich multi-semantic information. • Multi-semantic distribution information improve the rationality of hypergraphs. • Hypergraph and Multi-Semantic Distribution Information with node-edge message passing. Recently, Graph-based Few-Shot Learning (FSL) methods exhibit good generalization by mining relations among few samples with Graph Neural Networks. However, most Graph-based FSL methods consider only binary relations and ignore the multi-semantic information of the global context knowledge. We propose a framework of Multi-Semantic Hypergraph for FSL (MSH-FSL) to explore complex latent high-order multi-semantic relations among the few samples. By mining the complex relationship structure of multi-node and multi-semantics, more refined feature representation can be learned, which yields better classification robustness. Specifically, we first construct a novel Multi-Semantic Hypergraph by obtaining associated instances with different semantic features via orthogonal mapping. With the constructed hypergraph, we then develop the Hyergraph Neural Network along with a novel multi-generation hypergraph message passing so as to better leverage the complex latent semantic relations among samples. Finally, after a number of generations, the hyper-node representations embedded in the learned hypergraph become more accurate for obtaining few-shot prediction. In the 5-way 1-shot task of ResNet-12 on mini-Imagenet dataset, the multi-semantic hypergraph outperforms single-semantic graph by 3.1%, and with the proposed semantic-distribution message passing, the improvement can further reach 6.1%. [ABSTRACT FROM AUTHOR]

Visual object tracking is a challenging task considering illumination variation, occlusion, rotation, deformation and other problems. In this paper, we extend a Siamese INstance search Tracker (SINT) with model updating mechanism to improve its tracking robustness. SINT uses convolutional neural network (CNN) features, and compares the new frame features with the target features in the first frame. The candidate region with the highest similarity score is considered as the tracking result. However, SINT is not robust against large target variation because the matching model is not updated during the whole tracking process. To combat this defect, we propose an Ensemble Siamese Tracker (EST), where the final similarity score is also affected by the similarity with tracking results in recent frames instead of solely considering the first frame. Tracking results in recent frames are used to adjust the model for continuous target change. Meanwhile, we combine large displacement optical flow method with EST to further improve the performance (called EST+). We test the proposed EST and EST+ on a standard tracking benchmark OTB. It turns out the average overlap ratio of EST and EST+ increase 2.72% and 3.55% respectively compared with SINT on OTB 2013, which contains 51 video sequences. For the OTB 100, the average overlap ratio gain is 4.2%. [ABSTRACT FROM AUTHOR]