AmoebaNet

E899015

AmoebaNet is a convolutional neural network architecture discovered through evolutionary neural architecture search, known for achieving state-of-the-art image classification performance.

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AmoebaNet canonical 1

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Predicate Object
instanceOf convolutional neural network architecture
image classification model
neural architecture search result
applicationDomain image feature extraction
large-scale visual recognition
architectureStyle cell-based CNN
multi-branch convolutional architecture
basedOn cell-based architecture search space
benchmark CIFAR-10 NERFINISHED
CIFAR-100 NERFINISHED
ImageNet NERFINISHED
comparedTo NASNet NERFINISHED
PNASNet NERFINISHED
hand-designed CNN architectures
developedBy Google NERFINISHED
Google Brain NERFINISHED
discoveredBy Google Brain researchers NERFINISHED
field computer vision
deep learning
machine learning
framework TensorFlow (original implementation) NERFINISHED
hasProperty high top-1 accuracy on ImageNet
high top-5 accuracy on ImageNet
state-of-the-art performance on ImageNet at time of publication
hasVariant AmoebaNet-A NERFINISHED
AmoebaNet-B NERFINISHED
AmoebaNet-C NERFINISHED
implementedWith ReLU nonlinearities
batch normalization
stochastic gradient descent
influenced later NAS-based CNN architectures
introducedBy Alok Aggarwal NERFINISHED
Esteban Real NERFINISHED
Quoc V. Le NERFINISHED
Yanping Huang NERFINISHED
introducedIn “Regularized Evolution for Image Classifier Architecture Search” NERFINISHED
language Python (typical implementation)
optimizationObjective validation accuracy
publicationType research paper
searchAlgorithm aging evolution
evolutionary neural architecture search
task image classification
usesTechnique convolutional neural networks
evolutionary algorithms
neural architecture search
regularized evolution
yearOfIntroduction 2018

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