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gptkbp:instanceOf
|
deep learning model architecture
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gptkbp:activatedBy
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gptkb:ReLU
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gptkbp:architecture
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gptkb:convolutional_neural_network
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gptkbp:commonIn
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biomedical image analysis
medical image segmentation
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gptkbp:firstPublished
|
gptkb:U-Net:_Convolutional_Networks_for_Biomedical_Image_Segmentation
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gptkbp:frameworksAvailableIn
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gptkb:TensorFlow
gptkb:Keras
gptkb:PyTorch
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gptkbp:hasBatchNormalization
|
optional
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gptkbp:hasContractingPath
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yes
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|
gptkbp:hasConvolutionalLayers
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yes
|
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gptkbp:hasDecoder
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yes
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gptkbp:hasDownsamplingLayers
|
yes
|
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gptkbp:hasDropout
|
optional
|
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gptkbp:hasEncoder
|
yes
|
|
gptkbp:hasExpandingPath
|
yes
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|
gptkbp:hasPoolingLayers
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yes
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|
gptkbp:hasSkipConnections
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yes
|
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gptkbp:hasSymmetricStructure
|
yes
|
|
gptkbp:hasTransposedConvolutions
|
yes
|
|
gptkbp:hasUpsamplingLayers
|
yes
|
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gptkbp:hasVariant
|
gptkb:3D_U-Net
gptkb:Attention_U-Net
gptkb:ResUNet
gptkb:UNet++
gptkb:MultiResUNet
gptkb:Nested_U-Net
gptkb:U2-Net
gptkb:UNet3+
gptkb:V-Net
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
UNet architecture
|
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gptkbp:input
|
gptkb:illustrator
|
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gptkbp:inputShape
|
variable
|
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gptkbp:inspiredBy
|
gptkb:fully_convolutional_networks
|
|
gptkbp:introduced
|
gptkb:Olaf_Ronneberger
|
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gptkbp:introducedIn
|
2015
|
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gptkbp:lossFunction
|
cross-entropy
dice loss
|
|
gptkbp:notableFor
|
precise localization
works with few training images
end-to-end training
efficient use of training data
|
|
gptkbp:openSource
|
yes
|
|
gptkbp:output
|
segmentation map
|
|
gptkbp:outputActivationFunction
|
sigmoid
softmax
|
|
gptkbp:outputShape
|
variable
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gptkbp:usedFor
|
image segmentation
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gptkbp:bfsParent
|
gptkb:Stable_Diffusion_2.0
gptkb:Stable_Diffusion_model
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gptkbp:bfsLayer
|
6
|