Instance-wise sparsity is learned via feature decay regularization to accelerate deep model inference by pruning uninformative intermediate features per data instance.
Very deep convolutional networks for large-scale image recognition
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
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DMT uses identity and makeup encoders in a GAN to enable controllable makeup transfer from references and sampling of new styles from a prior distribution.
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Learning Instance-wise Sparsity for Accelerating Deep Models
Instance-wise sparsity is learned via feature decay regularization to accelerate deep model inference by pruning uninformative intermediate features per data instance.
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Disentangled Makeup Transfer with Generative Adversarial Network
DMT uses identity and makeup encoders in a GAN to enable controllable makeup transfer from references and sampling of new styles from a prior distribution.