ImageNet Large Scale Visual Recognition Challenge
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Cites methods from "ImageNet Large Scale Visual Recogni..."
...gresses, which aids the training. 4.2 ImageNet classification We applied Batch Normalization to a new variant of the Inception network Szegedy et al. (2014), trained on the ImageNet classification task Russakovsky et al. (2014). The network has a large number of convolutional and pooling layers, with a softmax layer to predict the image class, out of 1000 possibilities. Convolutional layers use ReLU as the nonlinearity. The...
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...This improves upon the previous best result, and exceeds the estimated accuracy of human raters according to (Russakovsky et al., 2014)....
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... test set), which improves upon the previous best result despite using 15X fewer parameters and lower resolution receptive field. Our system exceeds the estimated accuracy of human raters according to Russakovsky et al. (2014). For our ensemble, we used 6 networks. Each was based onBN-x30,modifiedvia someof thefollowing: increased initial weights in the convolutional layers; using Dropout (with the Dropout probability of 5%...
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...We applied Batch Normalization to a new variant of the Inception network (Szegedy et al., 2014), trained on the ImageNet classification task (Russakovsky et al., 2014)....
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References
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"ImageNet Large Scale Visual Recogni..." refers background in this paper
...3 – – – University of Oxford Karen Simonyan, Andrew Zisserman (Simonyan and Zisserman 2014) XYZ 11....
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49,639 citations
"ImageNet Large Scale Visual Recogni..." refers background or methods in this paper
...Instead we turn to designing novel crowdsourcing approaches for collecting large-scale annotations (Su et al., 2012; Deng et al., 2009, 2014)....
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...The ImageNet dataset (Deng et al., 2009) is the backbone of ILSVRC....
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...Constructing ImageNet was an effort to scale up an image classification dataset to cover most nouns in English using tens of millions of manually verified photographs (Deng et al., 2009)....
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...ei-Fei et al., 2004; Criminisi, 2004; Everingham et al., 2012; Xiao et al., 2010). Instead we turn to designing novel crowdsourcing approaches for collecting large-scale annotations (Su et al., 2012; Deng et al., 2009, 2014). Some of the 1000 object classes may not be as easy to annotate as the 20 categories of PASCAL VOC: e.g., bananas which appear in bunches may not be as easy to delineate as the basic-level cat...
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...New test images are collected and labeled especially for this competition and are not part of the previously published ImageNet dataset (Deng et al., 2009)....
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