In this paper, we propose an ultrafast automated model compression framework
called SeerNet for flexible network deployment. Conventional
non-differen-tiable methods discretely search the desirable compression policy
based on the accuracy from exhaustively trained lightweight models, and
existing differentiable methods optimize an extremely large supernet to obtain
the required compressed model for deployment. They both cause heavy
computational cost due to the complex compression policy search and evaluation
process. On the contrary, we obtain the optimal efficient networks by directly
optimizing the compression policy with an accurate performance predictor, where
the ultrafast automated model compression for various computational cost
constraint is achieved without complex compression policy search and
evaluation. Specifically, we first train the performance predictor based on the
accuracy from uncertain compression policies actively selected by efficient
evolutionary search, so that informative supervision is provided to learn the
accurate performance predictor with acceptable cost. Then we leverage the
gradient that maximizes the predicted performance under the barrier complexity
constraint for ultrafast acquisition of the desirable compression policy, where
adaptive update stepsizes with momentum are employed to enhance optimality of
the acquired pruning and quantization strategy. Compared with the
state-of-the-art automated model compression methods, experimental results on
image classification and object detection show that our method achieves
competitive accuracy-complexity trade-offs with significant reduction of the
search cost.Comment: Accepted to IJC