  Convolutional Neural Networks (CNNs) do not have a predictable recognition
behavior with respect to the input resolution change. This prevents the
feasibility of deployment on different input image resolutions for a specific
model. To achieve efficient and flexible image classification at runtime, we
employ meta learners to generate convolutional weights of main networks for
various input scales and maintain privatized Batch Normalization layers per
scale. For improved training performance, we further utilize knowledge
distillation on the fly over model predictions based on different input
resolutions. The learned meta network could dynamically parameterize main
networks to act on input images of arbitrary size with consistently better
accuracy compared to individually trained models. Extensive experiments on the
ImageNet demonstrate that our method achieves an improved accuracy-efficiency
trade-off during the adaptive inference process. By switching executable input
resolutions, our method could satisfy the requirement of fast adaption in
different resource-constrained environments. Code and models are available at
https://github.com/d-li14/SAN.
