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In this work, we present BasisNet which combines recent advancements inefficient neural network architectures, conditional computation, and earlytermination in a simple new form. Our approach incorporates a lightweight modelto preview the input and generate input-dependent combination coefficients,which later controls the synthesis of a more accurate specialist model to makefinal prediction. The two-stage model synthesis strategy can be applied to anynetwork architectures and both stages are jointly trained. We also show thatproper training recipes are critical for increasing generalizability for suchhigh capacity neural networks. On ImageNet classification benchmark, ourBasisNet with MobileNets as backbone demonstrated clear advantage onaccuracy-efficiency trade-off over several strong baselines. Specifically,BasisNet-MobileNetV3 obtained 80.3% top-1 accuracy with only 290M Multiply-Addoperations, halving the computational cost of previous state-of-the-art withoutsacrificing accuracy. With early termination, the average cost can be furtherreduced to 198M MAdds while maintaining accuracy of 80.0% on ImageNet.

BasisNet: Two-stage Model Synthesis for Efficient Inference