  Weight-sharing neural architecture search (NAS) is an effective technique for
automating efficient neural architecture design. Weight-sharing NAS builds a
supernet that assembles all the architectures as its sub-networks and jointly
trains the supernet with the sub-networks. The success of weight-sharing NAS
heavily relies on distilling the knowledge of the supernet to the sub-networks.
However, we find that the widely used distillation divergence, i.e., KL
divergence, may lead to student sub-networks that over-estimate or
under-estimate the uncertainty of the teacher supernet, leading to inferior
performance of the sub-networks. In this work, we propose to improve the
supernet training with a more generalized alpha-divergence. By adaptively
selecting the alpha-divergence, we simultaneously prevent the over-estimation
or under-estimation of the uncertainty of the teacher model. We apply the
proposed alpha-divergence based supernets training to both slimmable neural
networks and weight-sharing NAS, and demonstrate significant improvements.
Specifically, our discovered model family, AlphaNet, outperforms prior-art
models on a wide range of FLOPs regimes, including BigNAS, Once-for-All
networks, and AttentiveNAS. We achieve ImageNet top-1 accuracy of 80.0% with
only 444M FLOPs. Our code and pretrained models are available at
https://github.com/facebookresearch/AlphaNet.
