BasisNet: Two-stage Model Synthesis for Efficient Inference

Understand

In this work, we present BasisNet which combines recent advancements in efficient neural network architectures, conditional computation, and early termination in a simple new form.

• Our approach incorporates a lightweight model to preview the input and generate input-dependent combination coefficients, which later controls the synthesis of a more accurate specialist model to make final prediction.
• The two-stage model synthesis strategy can be applied to any network architectures and both stages are jointly trained.
• We also show that proper training recipes are critical for increasing generalizability for such high capacity neural networks.