EPNAS uses a progressive search policy with REINFORCE performance prediction to search neural architectures in parallel, supporting multiple resource constraints and outperforming ENAS and PNAS on CIFAR-10 and ImageNet in speed and accuracy.
Aggregated residual transformations for deep neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
Slim-Net uses stacked Slim Modules of depthwise separable convolutions to predict face attributes on CelebA at 91.24% accuracy with at least 25 times fewer parameters than comparable models.
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EPNAS: Efficient Progressive Neural Architecture Search
EPNAS uses a progressive search policy with REINFORCE performance prediction to search neural architectures in parallel, supporting multiple resource constraints and outperforming ENAS and PNAS on CIFAR-10 and ImageNet in speed and accuracy.
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Slim-CNN: A Light-Weight CNN for Face Attribute Prediction
Slim-Net uses stacked Slim Modules of depthwise separable convolutions to predict face attributes on CelebA at 91.24% accuracy with at least 25 times fewer parameters than comparable models.