A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects

Abstract: A convolutional neural network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention from both industry and academia in the past few years. The existing reviews mainly focus on CNN's applications in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered. In this review, we aim to provide some novel ideas and prospects in this fast-growing field. Besides, not only 2-D convolution but also 1-D and multidimensional ones are involved. First, this review introduces the history of CNN. Second, we provide an overview of various convolutions. Third, some classic and advanced CNN models are introduced; especially those key points making them reach state-of-the-art results. Fourth, through experimental analysis, we draw some conclusions and provide several rules of thumb for functions and hyperparameter selection. Fifth, the applications of 1-D, 2-D, and multidimensional convolution are covered. Finally, some open issues and promising directions for CNN are discussed as guidelines for future work.

NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning

Abstract: Offline reinforcement learning (RL) aims at learning effective policies from historical data without extra environment interactions. During our experience of applying offline RL, we noticed that previous offline RL benchmarks commonly involve significant reality gaps, which we have identified include rich and overly exploratory datasets, degraded baseline, and missing policy validation. In many real-world situations, to ensure system safety, running an overly exploratory policy to collect various data is prohibited, thus only a narrow data distribution is available. The resulting policy is regarded as effective if it is better than the working behavior policy; the policy model can be deployed only if it has been well validated, rather than accomplished the training. In this paper, we present a Near real-world offline RL benchmark, named NeoRL, to reflect these properties. NeoRL datasets are collected with a more conservative strategy. Moreover, NeoRL contains the offline training and offline validation pipeline before the online test, corresponding to real-world situations. We then evaluate recent state-of-the-art offline RL algorithms in NeoRL. The empirical results demonstrate that some offline RL algorithms are less competitive to the behavior cloning and the deterministic behavior policy, implying that they could be less effective in real-world tasks than in the previous benchmarks. We also disclose that current offline policy evaluation methods could hardly select the best policy. We hope this work will shed some light on future research and deploying RL in real-world systems.

A Text-Based CAPTCHA Cracking System with Generative Adversarial Networks

Abstract: As a multimedia security mechanism, CAPTCHAs are completely automated public turing test to tell computers and humans apart. Although cracking CAPTCHA has been explored for many years, it is still a challenging problem for real practice. In this demo, we present a text based CAPTCHA cracking system by using convolutional neural networks(CNN). To solve small sample problem, we propose to combine conditional deep convolutional generative adversarial networks(cDCGAN) and CNN, which makes a tremendous progress in accuracy. In addition, we also select multiple models with low pearson correlation coefficients for majority voting ensemble, which further improves the accuracy. The experimental results show that the system has great advantages and provides a new mean for cracking CAPTCHAs.

A convolutional neural network based approach towards real-time hard hat detection

Abstract: Health and safety management has been an important issue in construction industry. National regulations impose the using of hard hats in construction sites. However, there are often cases in which the construction workers neglect the regulations. It is desired to monitor the correct wearing of hard hat in real time and explore monitoring techniques facilitated by deep-learning algorithms. In this paper, a convolutional neural network based hard-hat detection algorithm is proposed. In this algorithm, the detection of construction workers and the hard hats are assisted by computer vision technique where deep learning model are trained to identify the proper wearing of hard hats. The optimization of the proposed neural networks can reduce the computational complexity while maintaining a relatively high recognition precision. Experiments have been performed using five different algorithms for comparison and results demonstrate that the proposed algorithm excels in the mAP and FPS performance metrics. The experimental results collected on an embedded platform reveal that the proposed algorithm presents a good candidate for similar applications where real-time deep-learning application is desired.

节水潜力预测研究综述

Abstract: 系统整理并分析了不同领域节水潜力预测方法的分类、原理、适用范围及研究现状,帮助从业人员针对实际问题快速选择模型。分析认为,基于公式模型的节水潜力预测方法收集数据较少,操作简捷,使用范围较广,但精确性不高;基于机器学习的节水潜力预测方法虽然收集的数据种类和数量较多,但构造出的预测模型使用范围广,精度高。针对节水潜力预测的现存问题,总结分析了其发展趋势。未来节水潜力预测的研究应根据不同产业和地域特点,引入深度学习、大数据等新技术,实现精细化节水潜力预测;同时加快完善基于互联网的节水潜力预测应用,实现集成数据收集、处理、预测、发布等功能于一体的节水社会化服务。

基于残差生成对抗网络的人脸图像复原

Abstract: 得益于计算机视觉的快速发展，人脸图像复原技术可以仅利用人脸的轮廓来生成完整的人脸图像。目前已有许多基于卷积神经网络和生成对抗网络等方法的人脸复原技术被提出，它们可以利用部分破损的人脸图像进行复原或者使用人脸轮廓直接生成人脸图像。然而，使用这些技术复原后的人脸图像在定性和定量分析时效果不够理想，并且复原时存在诸多的条件限制。因此，本文提出了一种基于残差生成对抗网络的人脸图像复原（FR-RGAN）方法，该方法借助深度卷积、残差网络和更小的卷积核提升了模型性能，利用人脸的轮廓复原面部局部细节，使其更加生动地呈现出来。实验结果表明，FR-RGAN在均方误差、峰值信噪比和结构相似度指标上比现有方法均有提升。