Search results with tag "Fea ture"
A Discriminative Feature Learning Approach for Deep Face ...
ydwen.github.ioA Discriminative Feature Learning Approach for Deep Face Recognition 501 Inthispaper,weproposeanewlossfunction,namelycenterloss,toefficiently enhance the discriminative power of the deeply learned features in neural net-works. Specifically, we learn a center (a vector with the same dimension as a fea-ture) for deep features of each class.
Rich Feature Hierarchies for Accurate Object Detection and ...
openaccess.thecvf.comFeature extraction. We extract a 4096-dimensional fea-ture vector from each region proposal using the Caffe [21] implementation of the CNN described by Krizhevsky et al. [22]. Features are computed by forward propagating a mean-subtracted 227 227 RGB image through five con-volutional layers and two fully connected layers. We refer
A Fast and Accurate Dependency Parser using Neural Networks
nlp.stanford.eduThe fea-ture generation of indicator features is gen-erally expensive — we have to concatenate some words, POS tags, or arc labels for gen-erating feature strings, and look them up in a huge table containing several millions of fea-tures. In our experiments, more than 95% of
ClassSR: A General Framework to Accelerate Super ...
openaccess.thecvf.comuse the LR image as input and upscale the feature maps at the end of the networks. LapSRN [12] introduces a deep laplacian pyramid network that gradually upscales the fea-ture maps. CARN [2] uses the group convolution to design a cascading residual network for fast processing. IMDN [9] extracts hierarchical features by splitting operations and
LTE-M DEPL OYMENT GUIDE T O BASIC FEA TURE SET …
www.gsma.comFEA TURE SET REQUIREMENT S JUNE 2019. ltE-m dEploymEnt GuidE to BaSic fEaturE SEt rEQuirEmEntS 1 ExEcutivE Summary 4 2 introduction 5 2.1 Overview 5 2.2 Scope 5 2.3 Definitions 6 2.4 Abbreviations 6 2.5 References 9 3 GSma minimum BaSElinE for ltE-m intEropEraBility - proBlEm StatEmEnt 10
Classification of Trash for Recyclability Status
cs229.stanford.edubased on which class model classifies the test datum with greatest margin. The features used for the SVM were SIFT fea-tures. On a high level, the SIFT algorithm finds blob like features in an image and describes each in 128 numbers. Specifically, the SIFT algorithm passes a dif-ference of Gaussian filter that varies ˙ values as
Tech report (v5) - arXiv
arxiv.orgFeature extraction. We extract a 4096-dimensional fea-ture vector from each region proposal using the Caffe [24] implementation of the CNN described by Krizhevsky et al. [25]. Features are computed by forward propagating a mean-subtracted 227 227 RGB image through five con-volutional layers and two fully connected layers. We refer
arXiv:1904.11492v1 [cs.CV] 25 Apr 2019
arxiv.org=1 as the fea-ture map of one input instance (e.g., an image or video), where Np is the number of positions in the feature map (e.g., Np=HW for image, Np=HWT for video). x and z denote the input and output of the non-local block, respectively, which have the same dimensions. The non-local block can then be expressed as
node2vec: Scalable Feature Learning for Networks
cs.stanford.edumize a reasonable objective required for scalable unsupervised fea-ture learning in networks. Classic approaches based on linear and non-linear dimensionality reduction techniques such as Principal Component Analysis, Multi-Dimensional Scaling and their exten-sions [3, 27, 30, 35] optimize an objective that transforms a repre-
Least Squares Optimization with L1-Norm Regularization
www.cs.ubc.cature selection method, and thus can give low variance fea-ture selection, compared to the high variance performance of typical subset selection techniques [1]. Furthermore, this does not come with a large disadvantage over subset selec-tion methods, since it …
DeepFM: A Factorization-Machine based Neural Network …
www.ijcai.orgSpecifically, the raw fea-ture input vector for CTR prediction is usually highly sparse3, super high-dimensional4, categorical-continuous-mixed, and grouped in fields (e.g., gender, location, age). This suggests an embedding layer to compress the input vector to a low-