2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)最新文献_第5页

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2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ictai.2019.00001

引用次数: 0

Harnessing GAN with Metric Learning for One-Shot Generation on a Fine-Grained Category 基于度量学习的GAN在细粒度分类上的一次性生成

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00130

Yusuke Ohtsubo, Tetsu Matsukawa, Einoshin Suzuki

{"title":"Harnessing GAN with Metric Learning for One-Shot Generation on a Fine-Grained Category","authors":"Yusuke Ohtsubo, Tetsu Matsukawa, Einoshin Suzuki","doi":"10.1109/ICTAI.2019.00130","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00130","url":null,"abstract":"We propose a GAN-based one-shot generation method on a fine-grained category, which represents a subclass of a category, typically with diverse examples. One-shot generation refers to a task of taking an image which belongs to a class not used in the training phase and then generating a set of new images belonging to the same class. Generative Adversarial Network (GAN), which represents a type of deep neural networks with competing generator and discriminator, has proven to be useful in generating realistic images. Especially DAGAN, which maps the input image to a low-dimensional space via an encoder and then back to the example space via a decoder, has been quite effective with datasets such as handwritten character datasets. However, when the class corresponds to a fine-grained category, DAGAN occasionally generates images which are regarded as belonging to other classes due to the rich variety of the examples in the class and the low dissimilarities of the examples among the classes. For example, it accidentally generates facial images of different persons when the class corresponds to a specific person. To circumvent this problem, we introduce a metric learning with a triplet loss to the bottleneck layer of DAGAN to penalize such a generation. We also extend the optimization algorithm of DAGAN to an alternating procedure for two types of loss functions. Our proposed method outperforms DAGAN in the GAN-test task for VGG-Face dataset and CompCars dataset by 5.6% and 4.8% in accuracy, respectively. We also conducted experiments for the data augmentation task and observed 4.5% higher accuracy for our proposed method over DAGAN for VGG-Face dataset.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115619919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Multiple Stopping Rule for Warm-Starting Sequential Selection 热启动顺序选择的最优多次停止规则

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00202

Mathilde Fekom, N. Vayatis, Argyris Kalogeratos

引用次数: 1

Automated Mechanism Design: Compact and Decomposition Linear Programming Models 自动化机构设计:紧凑和分解线性规划模型

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00031

B. Jaumard, Kia Babashahi Ashtiani, Nicolas Huin

引用次数: 0

Exploring Numerical Calculations with CalcNet 探索数值计算与CalcNet

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00192

Ashish Rana, A. Malhi, Kary Främling

{"title":"Exploring Numerical Calculations with CalcNet","authors":"Ashish Rana, A. Malhi, Kary Främling","doi":"10.1109/ICTAI.2019.00192","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00192","url":null,"abstract":"Neural networks are not great generalizers outside their training range i.e. they are good at capturing bias but might miss the overall concept. Important issues with neural networks is that when testing data goes outside training range they fail to predict accurate results. Hence, they loose the ability to generalize a concept. For systematic numeric exploration neural accumulators (NAC) and neural arithmetic logic unit(NALU) are proposed which performs excellent for simple arithmetic operations. But, major limitation with these units is that they can't handle complex mathematical operations & equations. For example, NALU can predict accurate results for multiplication operation but not for factorial function which is essentially composition of multiplication operations only. It is unable to comprehend pattern behind an expression when composition of operations are involved. Hence, we propose a new neural network structure effectively which takes in complex compositional mathematical operations and produces best possible results with small NALU based neural networks as its pluggable modules which evaluates these expression at unitary level in a bottom-up manner. We call this effective neural network as CalcNet, as it helps in predicting accurate calculations for complex numerical expressions even for values that are out of training range. As part of our study we applied this network on numerically approximating complex equations, evaluating biquadratic equations and tested reusability of these modules. We arrived at far better generalizations for complex arithmetic extrapolation tasks as compare to both only NALU layer based neural networks and simple feed forward neural networks. Also, we achieved even better results for our golden ratio based modified NAC and NALU structures for both interpolating and extrapolating tasks in all evaluation experiments. Finally, from reusability standpoint this model demonstrate strong invariance for making predictions on different tasks.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117279029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Optimizing Training using Information Theory-Based Curriculum Learning Factory 利用信息论课程学习工厂优化培训

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00218

Henok Ghebrechristos, G. Alaghband

{"title":"Optimizing Training using Information Theory-Based Curriculum Learning Factory","authors":"Henok Ghebrechristos, G. Alaghband","doi":"10.1109/ICTAI.2019.00218","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00218","url":null,"abstract":"We present a new system that can automatically generate input paths (syllabus) for a convolutional neural network to follow through a curriculum learning to improve training performance. Our system utilizes information-theoretic content measures of training samples to form syllabus at training time. We treat every sample as 2D random variable where a data point contained in the sample (such as a pixel) is modelled as an independent and identically distributed random variable (i.i.d) realization. We use several information theory methods to rank and determine when a sample is fed to a network by measuring its pixel composition and its relationship to other samples in the training set. Comparative evaluation of multiple state-of-the-art networks, including, GoogleNet, and VGG, on benchmark datasets demonstrate a syllabus that ranks samples using measures such as Joint Entropy between adjacent samples, can improve learning and significantly reduce the amount of training steps required to achieve desirable training accuracy. We present results that indicate our approach can reduce training loss by as much as a factor of 9 compared to conventional training.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115389060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evaluating Different Metric Configurations of an Evolutionary Wrapper for Attack Detection 评估用于攻击检测的进化包装器的不同度量配置

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00187

Javier Maldonado, M. Riff

引用次数: 3

Benchmarking Symbolic Execution Using Constraint Problems - Initial Results 使用约束问题对符号执行进行基准测试-初步结果

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00010

Sahil Verma, R. Yap

引用次数: 1

Adversarial Attack Against DoS Intrusion Detection: An Improved Boundary-Based Method 针对DoS入侵检测的对抗攻击:一种改进的基于边界的方法

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00179

Xiao Peng, Wei-qing Huang, Zhixin Shi

引用次数: 22

ECPNet: An Efficient Attention-Based Convolution Network with Pseudo-3D Block for Human Action Recognition ECPNet:一种有效的基于注意力的伪三维块卷积网络，用于人体动作识别

2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) Pub Date : 2019-11-01 DOI: 10.1109/ICTAI.2019.00089

Xiuping Bao, Jiabin Yuan, Bei Chen

{"title":"ECPNet: An Efficient Attention-Based Convolution Network with Pseudo-3D Block for Human Action Recognition","authors":"Xiuping Bao, Jiabin Yuan, Bei Chen","doi":"10.1109/ICTAI.2019.00089","DOIUrl":"https://doi.org/10.1109/ICTAI.2019.00089","url":null,"abstract":"Human action recognition has became an important task in computer vision and has received a significant amount of research interests in recent years. Convolutional Neural Network (CNN) has shown its power in image recognition task. While in the field of video recognition, it is still a challenge problem. In this paper, we introduce a high-efficient attention-based convolutional network named ECPNet for video understanding. ECPNet adopts the framework that is a consecutive connection of 2D CNN and pseudo-3D CNN. The pseudo-3D means we replace the traditional 3 × 3 × 3 kernel with two 3D convolutional filters shaped 1 × 3 × 3 and 3 × 1 × 1. Our ECPNet combines the advantages of both 2D and 3D CNNs: (1) ECPNet is an end-to-end network and can learn information of appearance from images and motion between frames. (2) ECPNet requires less computing resource and lower memory consumption than many state-of-art models. (3) ECPNet is easy to expand for different demands of runtime and classification accuracy. We evaluate the proposed model on three popular video benchmarks in human action recognition task: Kinetics-mini (split of full Kinetics), UCF101 and HMDB51. Our ECPNet achieves the excellent performance on above datasets with less time cost.","PeriodicalId":346657,"journal":{"name":"2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121404601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0