我们介绍了BasisNet，它以简单的新形式结合了高效神经网络体系结构，条件计算和早期终止的最新进展。我们的方法使用轻量级模型预览图像并生成依赖于输入的组合系数，该系数随后用于控制专家模型的合成，以进行更准确的最终预测。..

BasisNet: Two-stage Model Synthesis for Efficient Inference

We present BasisNet which combines recent advancements in efficient neural network architectures, conditional computation, and early termination in a simple new form. Our approach uses a lightweight model to preview an image and generate input-dependent combination coefficients, which are later used to control the synthesis of a specialist model for making more accurate final prediction.The two-stage model synthesis strategy can be used with any network architectures and both stages can be jointly trained end to end. We validated BasisNet on ImageNet classification with MobileNets as backbone, and demonstrated clear advantage on accuracy-efficiency trade-off over strong baselines such as EfficientNet (Tan & Le, 2019), FBNetV3 (Dai et al., 2020) and OFA (Cai et al., 2019). Specifically, BasisNet-MobileNetV3 obtained 80.3% top-1 accuracy with only 290M Multiply-Add operations (MAdds), halving the computational cost of previous state-of-the-art without sacrificing accuracy. Besides, since the first-stage lightweight model can independently make predictions, inference can be terminated early if the prediction is sufficiently confident. With early termination, the average cost can be further reduced to 198M MAdds while maintaining accuracy of 80.0%.