A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> From the Publisher: ::: The book provides the non-specialist with an introduction to quantitative evaluation of satellite and aircraft derived from remotely retrieved data. Each chapter covers the pros and cons of digital remotely sensed data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations. Problems conclude each chapter. <s> BIB001 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification. <s> BIB002 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day. <s> BIB003 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind. <s> BIB004 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> Can machine learning deliver AI? Theoretical results, inspiration from the brain and cognition, as well as machine learning experiments suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one would need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers, graphical models with many levels of latent variables, or in complicated propositional formulae re-using many sub-formulae. Each level of the architecture represents features at a different level of abstraction, defined as a composition of lower-level features. Searching the parameter space of deep architectures is a difficult task, but new algorithms have been discovered and a new sub-area has emerged in the machine learning community since 2006, following these discoveries. Learning algorithms such as those for Deep Belief Networks and other related unsupervised learning algorithms have recently been proposed to train deep architectures, yielding exciting results and beating the state-of-the-art in certain areas. Learning Deep Architectures for AI discusses the motivations for and principles of learning algorithms for deep architectures. By analyzing and comparing recent results with different learning algorithms for deep architectures, explanations for their success are proposed and discussed, highlighting challenges and suggesting avenues for future explorations in this area. <s> BIB005 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond. <s> BIB006 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> Purpose: Cerebrospinal fluid (CSF) segmentation in computed tomography (CT) is a key step in computer-aided detection (CAD) of acute ischemic stroke. Because of image noise, low contrast and intensity inhomogeneity, CSF segmentation has been a challenging task. A region-based active contour model, which is insensitive to contour initialization and robust to intensity inhomogeneity, was developed for segmenting CSF in brain CT images. Methods: The energy function of the region-based active contour model is composed of a range domain kernel function, a space domain kernel function, and an edge indicator function. By minimizing the energy function, the region of edge elements of the target could be automatically identified in images with less dependence on initial contours. The energy function was optimized by means of the deepest descent method with a level set framework. An overlap rate between segmentation results and the reference standard was used to assess the segmentation accuracy. The authors evaluated the performance of the proposed method on both synthetic data and real brain CT images. They also compared the performance level of our method to those of region-scalable fitting (RSF) and global convex segment (GCS) models. Results: For the experiment of CSF segmentation in 67 brain CT images, their method achieved an average overlap rate of 66% compared to the average overlap rates of 16% and 46% from the RSF model and the GCS model, respectively. Conclusions: Their region-based active contour model has the ability to achieve accurate segmentation results in images with high noise level and intensity inhomogeneity. Therefore, their method has great potential in the segmentation of medical images and would be useful for developing CAD schemes for acute ischemic stroke in brain CT images. <s> BIB007 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> We introduce HD (or “Hierarchical-Deep”) models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian (HB) models. Specifically, we show how we can learn a hierarchical Dirichlet process (HDP) prior over the activities of the top-level features in a deep Boltzmann machine (DBM). This compound HDP-DBM model learns to learn novel concepts from very few training example by learning low-level generic features, high-level features that capture correlations among low-level features, and a category hierarchy for sharing priors over the high-level features that are typical of different kinds of concepts. We present efficient learning and inference algorithms for the HDP-DBM model and show that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets. <s> BIB008 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> There are many innovations in imaging sensors designs. A bottleneck in evaluating proposed designs is the need to create optimal processing algorithms. I describe a pipeline architecture and simulation methods that automate the pipeline design. Article not available. <s> BIB009 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> Recently, high-dimensional shape data HDSD has been demonstrated to be informative in describing subcortical brain morphometry in several disorders. While HDSD may serve as a biomarker of disease, its high dimensionality may require careful treatment in its application to machine learning. Here, we compare several possible approaches for feature selection and pattern classification using HDSD. We explore the efficacy of three candidate feature selection FS methods: Guided Random Forest GRF, LASSO and no feature selection NFS. Each feature set was applied to three classifiers: Random Forest RF, Support Vector Machines SVM and Naive Bayes NB. Each model was cross-validated using two diagnostic contrasts: Alzheimer's Disease and mild cognitive impairment; each relative to matched controls. GRF and NFS outperformed LASSO as FS methods and were comparably competitive. NB underperformed relative to RF and SVM, which were comparable in performance. Our results advocate the NFS-RF approach for its speed, simplicity and interpretability. <s> BIB010 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> Recent advances in machine learning, specifically in deep learning with neural networks, has made a profound impact on fields such as natural language processing, image classification, and language modeling; however, feasibility and potential benefits of the approaches to metagenomic data analysis has been largely under-explored. Deep learning exploits many layers of learning nonlinear feature representations, typically in an unsupervised fashion, and recent results have shown outstanding generalization performance on previously unseen data. Furthermore, some deep learning methods can also represent the structure in a data set. Consequently, deep learning and neural networks may prove to be an appropriate approach for metagenomic data. To determine whether such approaches are indeed appropriate for metagenomics, we experiment with two deep learning methods: i) a deep belief network, and ii) a recursive neural network, the latter of which provides a tree representing the structure of the data. We compare these approaches to the standard multi-layer perceptron, which has been well-established in the machine learning community as a powerful prediction algorithm, though its presence is largely missing in metagenomics literature. We find that traditional neural networks can be quite powerful classifiers on metagenomic data compared to baseline methods, such as random forests. On the other hand, while the deep learning approaches did not result in improvements to the classification accuracy, they do provide the ability to learn hierarchical representations of a data set that standard classification methods do not allow. Our goal in this effort is not to determine the best algorithm in terms accuracy—as that depends on the specific application—but rather to highlight the benefits and drawbacks of each of the approach we discuss and provide insight on how they can be improved for predictive metagenomic analysis. <s> BIB011 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> Because art is inherently visual, the use of imaging has long been an important way to understand its structure, form, and history. Recently, new ways of engaging with objects from our shared cultural heritage are possible with advances in computation and imaging that allow scientists to analyze art noninvasively, historians to pose new social questions about the art, and the public to explore and interact with art in ways never before possible. There is a rich history in applying image processing techniques to conventional photographic images of works of art, many of which have been highlighted in previous special issues of IEEE Signal Processing Magazine (e.g., the 2008 and 2015 July issues). Building on these contributions, this article comprises a survey of techniques where computation is central to the image acquisition process. Known as computational imaging, the methods being pioneered in this field are increasingly relevant to cultural heritage applications because they leverage advances in image processing, acquisition, and display technologies that make scientific data readily comprehensible to a broad cohort of nontechnical researchers interested in understanding the visual content of art. Presently, only a small research community undertakes computational imaging of cultural heritage. Here we aim to introduce this growing new field to a larger research community by discussing: 1) the historic background of imaging of art, 2) the burgeoning present day community of researchers interested in computational imaging in the arts, and finally, 3) our vision for the future of this new field. <s> BIB012 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are also huge research opportunities in image computing with this new source of “big data”. It is well known that there is fundamental prognostic data embedded in pathology images. The ability to mine “sub-visual” image features from digital pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However the compelling opportunities in precision medicine offered by big digital pathology data come with their own set of computational challenges. Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images. Additionally there has been recent substantial interest in combining and fusing radiologic imaging and proteomics and genomics based measurements with features extracted from digital pathology images for better prognostic prediction of disease aggressiveness and patient outcome. Again there is a paucity of powerful tools for combining disease specific features that manifest across multiple different length scales. ::: ::: The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and pathology images and also combining digital pathology derived image measurements with molecular “omics” features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histopathology. <s> BIB013 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking, because they require very long training time and a large number of training samples. In this paper, we present an efficient and very robust tracking algorithm using a single convolutional neural network (CNN) for learning effective feature representations of the target object in a purely online manner. Our contributions are multifold. First, we introduce a novel truncated structural loss function that maintains as many training samples as possible and reduces the risk of tracking error accumulation. Second, we enhance the ordinary stochastic gradient descent approach in CNN training with a robust sample selection mechanism. The sampling mechanism randomly generates positive and negative samples from different temporal distributions, which are generated by taking the temporal relations and label noise into account. Finally, a lazy yet effective updating scheme is designed for CNN training. Equipped with this novel updating algorithm, the CNN model is robust to some long-existing difficulties in visual tracking, such as occlusion or incorrect detections, without loss of the effective adaption for significant appearance changes. In the experiment, our CNN tracker outperforms all compared state-of-the-art methods on two recently proposed benchmarks, which in total involve over 60 video sequences. The remarkable performance improvement over the existing trackers illustrates the superiority of the feature representations, which are learned purely online via the proposed deep learning framework. <s> BIB014 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> A study on one of the most important issues in a human action recognition task, i.e. how to create proper data representations with a high-level abstraction from large dimensional noisy video data, is carried out. Most of the recent successful studies in this area are mainly focused on deep learning. Deep learning methods have gained superiority to other approaches in the field of image recognition. In this survey, the authors first investigate the role of deep learning in both image and video processing and recognition. Owing to the variety and plenty of deep learning methods, the authors discuss them in a comparative form. For this purpose, the authors present an analytical framework to classify and to evaluate these methods based on some important functional measures. Furthermore, a categorisation of the state-of-the-art approaches in deep learning for human action recognition is presented. The authors summarise the significantly related works in each approach and discuss their performance. <s> BIB015 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> Abstract Distributed secure quantum machine learning (DSQML) enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server with the privacy data preserved. Moreover, DSQML can be extended to a more general case that the client does not have enough data, and resorts both the remote quantum server and remote databases to perform the secure machine learning. Here we propose a DSQML protocol that the client can classify two-dimensional vectors to different clusters, resorting to a remote small-scale photon quantum computation processor. The protocol is secure without leaking any relevant information to the Eve. Any eavesdropper who attempts to intercept and disturb the learning process can be noticed. In principle, this protocol can be used to classify high dimensional vectors and may provide a new viewpoint and application for future “big data”. <s> BIB016 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> We use deep learning for image processing by extracting image feature.The extraction has good performance in noisy or low-light circumstance.We optimize TV and L0 smoothing filter by the proposed feature extraction.The features can extract image structure features regardless the inputs directly. In this paper, we propose a novel low-level structure feature extraction for image processing based on deep neural network, stacked sparse denoising autoencoder (SSDA). The current image processing methods via deep learning are directly building and learning the end-to-end mappings between the input/output. Instead, we advocate the analysis of the first layer learning features from input data. With the learned low-level structure features, we improve two edge-preserving filters that are key to image processing tasks such as denoising, High Dynamic Range (HDR) compression and details enhancement. Due to the validity and superiority of the proposed feature extraction, the results computed by the two improved filters do not suffer from the drawbacks including halos, edge blurring, noise amplification and over-enhancement. More importantly, we demonstrate that the features trained from natural images are not specific and can extract the structure features of infrared images. Hence, it is feasible to handle tasks by using the trained features directly. <s> BIB017 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> With the availability of big data and advanced hardware technologies, deep learning has been applied in different applications, such as self-driving cars and face recognition. While considering various sources of uncertainty and balancing multiple objectives of a system, the hardware implementation of deep learning needs continuous model updating and intensive synaptic weight storage, and SRAM is critical for the overall performance and energy efficiency. In this brief, we introduce offline data mining to the hardware design process, and the discovered data knowledge combined with a data-driven hardware design technique enables a more intelligent memory with better trade-off between energy efficiency, cost, and classification accuracy, thereby helping relieve the huge burden of data storage in deep learning systems. A 45 nm 64 kbits (256 words $\boldsymbol {\times } \,\, 256$ bits) synaptic SRAM is presented that enables 45.6% active power saving and 83.2% leakage power saving, with low implementation cost (3.17%) and less than 1% degradation in classification accuracy. <s> BIB018 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> Understanding Earth's subsurface structures has been and continues to be an essential component of various applications such as environmental monitoring, carbon sequestration, and oil and gas exploration. By viewing the seismic volumes that are generated through the processing of recorded seismic traces, researchers were able to learn from applying advanced image processing and computer vision algorithms to effectively analyze and understand Earth's subsurface structures. In this article, we first summarize the recent advances in this direction that relied heavily on the fields of image processing and computer vision. Second, we discuss the challenges in seismic interpretation and provide insights and some directions to address such challenges using emerging machine-learning algorithms. <s> BIB019 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> An increasing number of image processing applications require an automated quality prediction of the visual content as perceived by humans. Since, sparse coding is suggested to be an underlying strategy of the brain’s neural system, it would be logical to assume that specific tasks like quality assessment also attempt to adhere to this strategy. However, existing perceptual quality predictors, often mimicking the different stages of the human visual system and deploying machine learning strategies, such as neural networks, rarely integrate the concept of sparse coding in their design. In this paper, we first investigate the validity of such assumption by performing an empirical analysis on the relation between the structural information of the scene—captured via sparseness significance—and perceptual quality. Subsequently, we propose a new approach to integrate the significance of sparse coding features in the future imagequality measure (IQM) designs. We utilize the Fourier transform as a case study, which leads to a new IQM called sparseness significance ranking measure (SSRM). This measure essentially deploys a Fourier basis for sparse coding, a ranking mechanism based upon the amplitudes of the sparse coefficients and subsequently a complex correlation metric that assesses the correspondence between the ranked coefficient amplitude profiles of the reference and the distorted image. Moreover, we introduce a new methodology, namely separation ratio analysis, to assess the prediction quality of individual features or quality predictors given a target perceptual quality. The quality predictions by the proposed SSRM show excellent compatibility with perceptual quality scores. A set of routine benchmarking experiments utilizing the LIVE and CSIQ, IVC and TID2008 databases indicates a highly competitive performance with state of the art IQMs. Moreover, it delivers this performance at a low computational cost. <s> BIB020 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> I. INTRODUCTION <s> Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning network. Next, pixel-wise prediction is obtained by convolving the trained classifier over input image. An experiment on three public and one industrial data set is carried out. The experiment involves two tasks: 1) image classification and 2) defect segmentation. The results of proposed algorithm are compared against several best benchmarks in literature. In the classification tasks, the proposed method improves accuracy by 0.66%–25.50%. In the segmentation tasks, the proposed method reduces error escape rates by 6.00%–19.00% in three defect types and improves accuracies by 2.29%–9.86% in all seven defect types. In addition, the proposed method achieves 0.0% error escape rate in the segmentation task of industrial data. <s> BIB021
Since images play an essential role in our daily life, and as the advances in computer information collection systems, one can obtain more and more image sets, but most of them cannot be processed manually BIB009 - BIB015 . Hence image processing becomes attractive since much of this image information can be represented and processed digitally. With the fast computers and signal processors available in the 2000s, image processing has become the most common processing The associate editor coordinating the review of this manuscript and approving it for publication was Fanbiao Li . technique to be used in medical images, remote sensing image and nature image BIB012 (see Fig.1 ), and generally, is used because it is not only the most versatile method but also the cheapest BIB019 . Image processing has been playing a more vital and essential role in various information access systems to enhance the cognition level and facilitate decisionmaking process  . In pattern recognition and machine learning, the commonly used image processing includes image generation, image compression and encoding, image deblurring, super-resolution, image segmentation, classification and object recognition, change detection, image annotation, and image retrieval, etc. In particular, machine learning VOLUME 7, 2019 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ FIGURE 1. Three typical sample type of image processing. Left: remote sensing image BIB001 , Middle: medical images BIB007 , Right: nature image  . techniques has been widely and successfully applied to image processing research BIB013 . Compared with the nonlearning-based methods that might not precisely translate domain knowledge into rules or features, machine learning acquires its knowledge from data representations BIB020 . In other words, while the low-level features can be hand-crafted with great success for some specific data and tasks, designing useful features for new data and tasks requires new domain knowledge since most hand-crafted features cannot adapt to new conditions BIB014 . Besides, conventional machine learning techniques usually do not directly deal with raw data but heavily rely on the data representations, which further to require considerable domain expertise and sophisticated engineering BIB016 - BIB010 . Learning higher-level features from the data of interest is considered as a plausible way to remedy the limitation of hand-crafted features BIB008 - BIB011 . A successful example of such methods is learning through the framework of deep networks, which draws significant attention recently. Compared with hand-crafted features, learned multiple levels of representations require less human interventions and provide much better performance. There is no doubt that deep learning techniques have made significant advances in image processing. The core idea of deep learning is to discover multiple levels of representation and automatically learns the representations, with the hope that higher-level features represent more abstract semantics of the data BIB017 . Meanwhile, such abstract representations learned from deep networks are expected to provide more invariance to intra-class variability BIB004 , BIB005 . To further improve the readability of this survey, we present the phrases commonly and explanations that appeared in deep learning in TABLE 1. As is known to all, one key ingredient in the success of deep learning in image processing is the use of CNNs BIB002 , which includes a convolutional flow module stacked on top of each other. Moreover, each convolutional flow module comprises four parts-convolution filter bank layer, feature maps pooling layer, nonlinear processing layer, and BN. Many classical variations on deep CNNs have been proposed to different tasks in image processing, such as LeNet, AlexNet, GoogleNet, VGGNet, R-CNN, YOLO, SSD, SqueezeNet, ResNet, DenseNet  , SegNet, and DCGAN, etc. and their success is usually justified empirically BIB003 - BIB006 . With the development of computing power and data scale, many classical deep learning algorithms, which based on the SGD method, have verified, but only under an apparent theoretical premise can we know what the worst is. At present, The uncertainty of deep learning is mainly reflected in three aspects BIB018 , • The BP algorithm for updating weights and bias causes gradient diffusion or explosion. • The initialization method of network weights/bias affects the solution to non-convex optimization problems. • The regularization methods affect the generalization performance of the deep learning model. Besides, many classical deep learning algorithms rely on a massive amount of datasets because the singularity of the sample does not effectively remove, and the spatial logic or structure relationship of the target for the sample is ignored, etc  . According to the different requirements of the application task, at the same time, to solve the existing problems of classical deep learning (such as AE, CNN, DBN, RNN, and GAN, etc.), a variety of new generation algorithms and frameworks of deep learning have proposed. These latest frameworks not only improved the network generalization performance and significantly improved the efficiency of optimization but also enriched the research system of deep learning  , BIB021 .