Taylor-expansion importance scoring enables layer-agnostic pruning of neural networks that outperforms prior methods on ImageNet accuracy-FLOPs trade-offs.
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2019 2verdicts
UNVERDICTED 2representative citing papers
A CNN using pixel-wise binary supervision detects face spoofs, reporting 0% HTER on Replay Mobile and 0.42% ACER on OULU Protocol-1.
citing papers explorer
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Importance Estimation for Neural Network Pruning
Taylor-expansion importance scoring enables layer-agnostic pruning of neural networks that outperforms prior methods on ImageNet accuracy-FLOPs trade-offs.
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Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
A CNN using pixel-wise binary supervision detects face spoofs, reporting 0% HTER on Replay Mobile and 0.42% ACER on OULU Protocol-1.