  Neural architecture search (NAS) aims to discover network architectures with
desired properties such as high accuracy or low latency. Recently,
differentiable NAS (DNAS) has demonstrated promising results while maintaining
a search cost orders of magnitude lower than reinforcement learning (RL) based
NAS. However, DNAS models can only optimize differentiable loss functions in
search, and they require an accurate differentiable approximation of
non-differentiable criteria. In this work, we present UNAS, a unified framework
for NAS, that encapsulates recent DNAS and RL-based approaches under one
framework. Our framework brings the best of both worlds, and it enables us to
search for architectures with both differentiable and non-differentiable
criteria in one unified framework while maintaining a low search cost. Further,
we introduce a new objective function for search based on the generalization
gap that prevents the selection of architectures prone to overfitting. We
present extensive experiments on the CIFAR-10, CIFAR-100, and ImageNet datasets
and we perform search in two fundamentally different search spaces. We show
that UNAS obtains the state-of-the-art average accuracy on all three datasets
when compared to the architectures searched in the DARTS space. Moreover, we
show that UNAS can find an efficient and accurate architecture in the
ProxylessNAS search space, that outperforms existing MobileNetV2 based
architectures. The source code is available at https://github.com/NVlabs/unas .
