News. Phi Delta Epsilon最新文献_第10页

Data Augmentation for Semantic Segmentation in the Context of Carbon Fiber Defect Detection using Adversarial Learning 基于对抗学习的碳纤维缺陷检测中语义分割的数据增强

News. Phi Delta Epsilon Pub Date : 2020-01-01 DOI: 10.5220/0009823500590067

Silvan Mertes, A. Margraf, Christoph Kommer, Steffen Geinitz, E. André

{"title":"Data Augmentation for Semantic Segmentation in the Context of Carbon Fiber Defect Detection using Adversarial Learning","authors":"Silvan Mertes, A. Margraf, Christoph Kommer, Steffen Geinitz, E. André","doi":"10.5220/0009823500590067","DOIUrl":"https://doi.org/10.5220/0009823500590067","url":null,"abstract":": Computer vision systems are popular tools for monitoring tasks in highly specialized production environ-ments. The training and conﬁguration, however, still represents a time-consuming task in process automation. Convolutional neural networks have helped to improve the ability to detect even complex anomalies withouth exactly modeling image ﬁlters and segmentation strategies for a wide range of application scenarios. In recent publications, image-to-image translation using generative adversarial networks was introduced as a promising strategy to apply patterns to other domains without prior explicit mapping. We propose a new approach for generating augmented data to enable the training of convolutional neural networks for semantic segmentation with a minimum of real labeled data. We present qualitative results and demonstrate the application of our system on textile images of carbon ﬁbers with structural anomalies. This paper compares the potential of image-to-image translation networks with common data augmentation strategies such as image scaling, rotation or mirroring. We train and test on image data acquired from a high resolution camera within an industrial monitoring use case. The experiments show that our system is comparable to common data augmentation approaches. Our approach extends the toolbox of semantic segmentation since it allows for generating more problem-speciﬁc training data from sparse input.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"8 1","pages":"59-67"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80123443","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: 7

Attention-based Text Recognition in the Wild 野外基于注意力的文本识别

News. Phi Delta Epsilon Pub Date : 2020-01-01 DOI: 10.5220/0009970200420049

Zhi-Chen Yan, Stephanie A. Yu

引用次数: 0

Run 运行

News. Phi Delta Epsilon Pub Date : 2010-01-01 DOI: 10.1109/DELTA.2010.28

P. Beckett, H. Rudolph

引用次数: 0

Optimization of Multipartite Table Methods to Approximate the Elementary Functions 逼近初等函数的多部表法的优化

News. Phi Delta Epsilon Pub Date : 2004-01-01 DOI: 10.1109/DELTA.2004.10051

Huaping Liu, Chengde Han

引用次数: 0

Otolaryngology in World War II. 二战时期的耳鼻喉科。

News. Phi Delta Epsilon Pub Date : 1947-03-01

F LEDERER

引用次数: 0

Progress in surgery in war and peace. 战争与和平时期外科手术的进展。

News. Phi Delta Epsilon Pub Date : 1946-12-01

A M GLICKMAN

引用次数: 0

A plan to eradicate the common cold. 根除普通感冒的计划。

News. Phi Delta Epsilon Pub Date : 1946-10-01

N D FABRICANT

引用次数: 0

Accelerating Matrix Factorization by Overparameterization 用过参数化加速矩阵分解

News. Phi Delta Epsilon Pub Date : 1900-01-01 DOI: 10.5220/0009885600890097

Pu Chen, Hung-Hsuan Chen

{"title":"Accelerating Matrix Factorization by Overparameterization","authors":"Pu Chen, Hung-Hsuan Chen","doi":"10.5220/0009885600890097","DOIUrl":"https://doi.org/10.5220/0009885600890097","url":null,"abstract":": This paper studies overparameterization on the matrix factorization (MF) model. We conﬁrm that overparameterization can signiﬁcantly accelerate the optimization of MF with no change in the expressiveness of the learning model. Consequently, modern applications on recommendations based on MF or its variants can largely beneﬁt from our discovery. Speciﬁcally, we theoretically derive that applying the vanilla stochastic gradient descent (SGD) on the overparameterized MF model is equivalent to employing gradient descent with momentum and adaptive learning rate on the standard MF model. We empirically compare the overparameterized MF model with the standard MF model based on various optimizers, including vanilla SGD, AdaGrad, Adadelta, RMSprop, and Adam, using several public datasets. The experimental results comply with our analysis – overparameterization converges faster. The overparameterization technique can be applied to various learning-based recommendation models, including deep learning-based recommendation models, e.g., SVD++, nonnegative matrix factorization (NMF), factorization machine (FM), NeuralCF, Wide&Deep, and DeepFM. Therefore, we suggest utilizing the overparameterization technique to accelerate the training speed for the learning-based recommendation models whenever possible, especially when the size of the training dataset is large.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"20 1","pages":"89-97"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74441175","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: 13