AutoHAS: Differentiable Hyper-parameter and Architecture Search

Neural Architecture Search (NAS) has achieved significant progress in pushing state-of-the-art performance. While previous NAS methods search for different network architectures with the same hyper-parameters, we argue that such search would lead to sub-optimal results. We empirically observe that different architectures tend to favor their own hyper-parameters. In this work, we extend NAS to a broader and more practical space by combining hyper-parameter and architecture search. As architecture choices are often categorical whereas hyper-parameter choices are often continuous, a critical challenge here is how to handle these two types of values in a joint search space. To tackle this challenge, we propose AutoHAS, a differentiable hyper-parameter and architecture search approach, with the idea of discretizing the continuous space into a linear combination of multiple categorical basis. A key element of AutoHAS is the use of weight sharing across all architectures and hyper-parameters which enables efficient search over the large joint search space. Experimental results on MobileNet/ResNet/EfficientNet/BERT show that AutoHAS significantly improves accuracy up to 2% on ImageNet and F1 score up to 0.4 on SQuAD 1.1, with search cost comparable to training a single model. Compared to other AutoML methods, such as random search or Bayesian methods, AutoHAS can achieve better accuracy with 10x less compute cost.