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arXiv · 1904.04971

CondConv: Conditionally Parameterized Convolutions for Efficient Inference

Brandon Yang·Gabriel Bender·Quoc V. Le·Jiquan Ngiam
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Abstract

Convolutional layers are one of the basic building blocks of modern deep neural networks. One fundamental assumption is that convolutional kernels should be shared for all examples in a dataset. We propose conditionally parameterized convolutions (CondConv), which learn specialized convolutional kernels for each example. Replacing normal convolutions with CondConv enables us to increase the size and capacity of a network, while maintaining efficient inference. We demonstrate that scaling networks with CondConv improves the performance and inference cost trade-off of several existing convolutional neural network architectures on both classification and detection tasks. On ImageNet classification, our CondConv approach applied to EfficientNet-B0 achieves state-of-the-art performance of 78.3% accuracy with only 413M multiply-adds. Code and checkpoints for the CondConv Tensorflow layer and CondConv-EfficientNet models are available at: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/condconv.

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BibTeXRIS

Brandon Yang, Gabriel Bender, Quoc V. Le, Jiquan Ngiam. 2019-04-10. CondConv: Conditionally Parameterized Convolutions for Efficient Inference. https://arxiv.org/abs/1904.04971

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