卷积神经网络结构优化综述

近年来,卷积神经网络(Convolutional neural network,CNNs)在计算机视觉、自然语言处理、语音识别等领域取得了突飞猛进的发展,其强大的特征学习能力引起了国内外专家学者广泛关注.然而,由于深度卷积神经网络普遍规模庞大、计算度复杂,限制了其在实时要求高和资源受限环境下的应用.对卷积神经网络的结构进行优化以压缩并加速现有网络有助于深度学习在更大范围的推广应用,目前已成为深度学习社区的一个研究热点.本文整理了卷积神经网络结构优化技术的发展历史、研究现状以及典型方法,将这些工作归纳为网络剪枝与稀疏化、张量分解、知识迁移和精细模块设计4个方面并进行了较为全面的探讨.最后,本文对当前研究的热点与难点作了分析和总结,并对网络结构优化领域未来的发展方向和应用前景进行了展望.     

深度卷积神经网络在计算机视觉中的应用研究综述

随着大数据时代的到来,含更多 隐含层的深度卷积神经网络(Convolutional neural networks, CNNs)具有更复杂的网络结构,与传统机器学习方法相比具有更强大的特征学习和特征表达能力。使用深度学习算法训练的卷积神经网络模型自提出以来在计算机视觉领域的多个大规模识别任务上取得了令人瞩目的 成绩。本文首先简要介绍深度学习和卷积神经网络的兴起与展,概述卷积神经网络的基本模型结构、卷积特征提取和池化操作。然后综述了基于深度学习的卷积神经网络模型在图像分类、物体检测、姿态估计、图像分割和人脸识别等多个计算机视觉应用领域中的研究现状 和发展趋势,主要从典型的网络结构的构建、训练方法和性能表现3个方面进行介绍。最后对目前研究中存在的一些问题进行简要的总结和讨论,并展望未来发展的新方向。     

A survey on deep learning-based fine-grained object classification and semantic segmentation

The deep learning technology has shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation. In particular, recent advances of deep learning techniques bring encouraging performance to fine-grained image classification which aims to distinguish subordinate-level categories, such as bird species or dog breeds. This task is extremely challenging due to high intra-class and low inter-class variance. In this paper, we review four types of deep learning based fine-grained image classification approaches, including the general convolutional neural networks (CNNs), part detection based, ensemble of networks based and visual attention based fine-grained image classification approaches. Besides, the deep learning based semantic segmentation approaches are also covered in this paper. The region proposal based and fully convolutional networks based approaches for semantic segmentation are introduced respectively.     

基于深度卷积特征的细粒度图像分类研究综述

细粒度图像分类问题是计算机视觉领域一项极具挑战的研究课题,其目标是对子类进行识别,如区分不同种类的鸟.由于子类别间细微的类间差异和较大的类内差异,传统的分类算法不得不依赖于大量的人工标注信息.近年来,随着深度学习的发展,深度卷积神经网络为细粒度图像分类带来了新的机遇.大量基于深度卷积特征算法的提出,促进了该领域的快速发展.本文首先从该问题的定义以及研究意义出发,介绍了细粒度图像分类算法的发展现状.之后,从强监督与弱监督两个角度对比分析了不同算法之间的差异,并比较了这些算法在常用数据集上的性能表现.最后,我们对这些算法进行了总结,并讨论了该领域未来可能的研究方向及其面临的挑战.     

Intelligent Visual Media Processing: When Graphics Meets Vision

The computer graphics and computer vision communities have been working closely together in recent years, and a variety of algorithms and applications have been developed to analyze and manipulate the visual media around us. There are three major driving forces behind this phenomenon: 1) the availability of big data from the Internet has created a demand for dealing with the ever-increasing, vast amount of resources; 2) powerful processing tools, such as deep neural networks, provide effective ways for learning how to deal with heterogeneous visual data; 3) new data capture devices, such as the Kinect, the bridge between algorithms for 2D image understanding and 3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey recent research on how computer vision techniques benefit computer graphics techniques and vice versa, and cover research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest possible further research directions.     

On the automated,evolutionary design of neural networks: past,present, and future

Neuroevolution is the name given to a field of computer science that applies evolutionary computation for evolving some aspects of neural networks. After the AI Winter came to an end, neural networks reemerged to solve a great variety of problems. However, their usage requires designing their topology, a decision with a potentially high impact on performance. Whereas many works have tried to suggest rules-of-thumb for designing topologies, the truth is that there are not analytic procedures for determining the optimal one for a given problem, and trial-and-error is often used instead. Neuroevolution arose almost 3 decades ago, with some works focusing on the evolutionary design of the topology and most works describing techniques for learning connection weights. Since then, evolutionary computation has been proved to be a convenient approach for determining the topology and weights of neural networks, and neuroevolution has been applied to a great variety of fields. However, for more than 2 decades neuroevolution has mainly focused on simple artificial neural networks models, far from today’s deep learning standards. This is insufficient for determining good architectures for modern networks extensively used nowadays, which involve multiple hidden layers, recurrent cells, etc. More importantly, deep and convolutional neural networks have become a de facto standard in representation learning for solving many different problems, and neuroevolution has only focused in this kind of networks in very recent years, with many works being presented in 2017 onward. In this paper, we review the field of neuroevolution during the last 3 decades. We will put the focus on very recent works on the evolution of deep and convolutional neural networks, which is a new but growing field of study. To the best of our knowledge, this is the best survey reviewing the literature in this field, and we have described the features of each work as well as their performance on well-known databases when available. This work aims to provide a complete reference of all works related to neuroevolution of convolutional neural networks up to the date. Finally, we will provide some future directions for the advancement of this research area.

     

胶囊神经网络研究现状与未来的浅析

当今时代的人工智能技术迅速发展,推动了社会的巨大进步。深度学习作为人工智能领域重要的一部分,具有非常广阔的应用前景,近年来,越来越多的专家学者开始研究深度学习领域相关技术,比较典型的两个方向就是自然语言处理和计算机视觉,其中计算机视觉的发展大力引领着深度学习领域的进步。介绍了卷积神经网络的经典模型和深度学习中新型神经网络模型--胶囊网络以及其动态路由算法,并对比了二者的优劣性。对胶囊网络的应用给予综述,以图像和文本两方面来阐述胶囊网络的应用领域和优势所在。最后进行概括总结,并展望了胶囊网络可能的改进方向。     

计算机视觉下的车辆目标检测算法综述

车辆目标检测是基于计算机视觉的目标检测领域的一个重要应用领域,近年来随着深度学习在图像分类方面取得的巨大进展,机器视觉技术结合深度学习方法的车辆目标检测算法逐渐成为该领域的研究重点和热点。介绍了基于机器视觉的车辆目标检测的任务、难点与发展现状,以及深度学习方法中几种具有代表性的卷积神经网络模型,通过这些网络模型衍生出的two stage、one stage车辆目标检测算法和用于模型训练的相关数据集与检测效果评价标准,对其存在的问题及未来可能的发展方向进行了讨论。     

深度学习在组织病理学中的应用综述

组织病理学是临床上肿瘤诊断的金标准,直接关系到治疗的开展与预后的评估。来自临床的需求为组织病理诊断提出了质量与效率两个方面的挑战。组织病理诊断涉及大量繁重的病理切片判读任务,高度依赖医生的经验,但病理医生的培养周期长,人才储备缺口巨大,病理科室普遍超负荷工作。近年来出现的基于深度学习的组织病理辅助诊断方法可以帮助医生提高诊断工作的精度与速度,缓解病理诊断资源不足的问题,引起了研究人员的广泛关注。本文初步综述深度学习方法在组织病理学中的相关研究工作。介绍了组织病理诊断的医学背景,整理了组织病理学领域的主要数据集,重点介绍倍受关注的乳腺癌、淋巴结转移癌、结肠癌的病理数据及其分析任务。本文归纳了数据的存储与处理、模型的设计与优化以及小样本与弱标注学习这3项需要解决的技术问题。围绕这些问题,本文介绍了包括数据存储、数据预处理、分类模型、分割模型、迁移学习和多示例学习等相关研究工作。最后总结了面向组织病理学诊断的深度学习方法研究现状,并指出当下研究工作可能的改进方向。     

Medical image semantic segmentation based on deep learning

The image semantic segmentation has been extensively studying. The modern methods rely on the deep convolutional neural networks, which can be trained to address this problem. A few years ago networks require the huge dataset to be trained. However, the recent advances in deep learning allow training networks on the small datasets, which is a critical issue for medical images, since the hospitals and research organizations usually do not provide the huge amount of data. In this paper, we address medical image semantic segmentation problem by applying the modern CNN model. Moreover, the recent achievements in deep learning allow processing the whole image per time by applying concepts of the fully convolutional neural network. Our qualitative and quantitate experiment results demonstrated that modern CNN can successfully tackle the medical image semantic segmentation problem.