  Practical networks for edge devices adopt shallow depth and small
convolutional kernels to save memory and computational cost, which leads to a
restricted receptive field. Conventional efficient learning methods focus on
lightweight convolution designs, ignoring the role of the receptive field in
neural network design. In this paper, we propose the Meta-Pooling framework to
make the receptive field learnable for a lightweight network, which consists of
parameterized pooling-based operations. Specifically, we introduce a
parameterized spatial enhancer, which is composed of pooling operations to
provide versatile receptive fields for each layer of a lightweight model. Then,
we present a Progressive Meta-Pooling Learning (PMPL) strategy for the
parameterized spatial enhancer to acquire a suitable receptive field size. The
results on the ImageNet dataset demonstrate that MobileNetV2 using Meta-Pooling
achieves top1 accuracy of 74.6\%, which outperforms MobileNetV2 by 2.3\%.
