AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling
This work provides an incremental improvement in neural architecture search efficiency and accuracy for researchers and practitioners designing computer vision models.
This paper introduces AttentiveNAS, a method that improves neural architecture search by focusing on an attentive sampling strategy during training. It achieves a top-1 accuracy range of 77.3% to 80.7% on ImageNet, outperforming state-of-the-art models like BigNAS and Once-for-All networks, and reaches 80.1% accuracy with 491 MFLOPs.
Neural architecture search (NAS) has shown great promise in designing state-of-the-art (SOTA) models that are both accurate and efficient. Recently, two-stage NAS, e.g. BigNAS, decouples the model training and searching process and achieves remarkable search efficiency and accuracy. Two-stage NAS requires sampling from the search space during training, which directly impacts the accuracy of the final searched models. While uniform sampling has been widely used for its simplicity, it is agnostic of the model performance Pareto front, which is the main focus in the search process, and thus, misses opportunities to further improve the model accuracy. In this work, we propose AttentiveNAS that focuses on improving the sampling strategy to achieve better performance Pareto. We also propose algorithms to efficiently and effectively identify the networks on the Pareto during training. Without extra re-training or post-processing, we can simultaneously obtain a large number of networks across a wide range of FLOPs. Our discovered model family, AttentiveNAS models, achieves top-1 accuracy from 77.3% to 80.7% on ImageNet, and outperforms SOTA models, including BigNAS and Once-for-All networks. We also achieve ImageNet accuracy of 80.1% with only 491 MFLOPs. Our training code and pretrained models are available at https://github.com/facebookresearch/AttentiveNAS.