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In this paper, we propose Efficient Progressive Neural Architecture Search (EPNAS), a neural architecture search (NAS) that efficiently handles large search space through a novel progressive search policy with performance prediction based on REINFORCE [37]. EPNAS is designed to search target networks in parallel, which is more scalable on parallel systems such as GPU/TPU clusters. More importantly, EPNAS can be generalized to architecture search with multiple resource constraints, e.g., model size, compute complexity or intensity, which is crucial for deployment in widespread platforms such as mobile and cloud. We compare EPNAS against other state-of-the-art (SoTA) network architectures (e.g., MobileNetV2 [39]) and efficient NAS algorithms (e.g., ENAS [34], and PNAS [27]) on image recognition tasks using CIFAR10 and ImageNet. On both datasets, EPNAS is superior w.r.t. architecture searching speed and recognition accuracy
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Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?
Existing Neural Architecture Search (NAS) methods either encode neural architectures using discrete encodings that do not scale well, or adopt supervised learning-based methods to jointly learn architecture representations and optimize architecture search on such representations which incurs search bias. Despite the widespread use, architecture representations learned in NAS are still poorly understood. We observe that the structural properties of neural architectures are hard to preserve in the latent space if architecture representation learning and search are coupled, resulting in less effective search performance. In this work, we find empirically that pre-training architecture representations using only neural architectures without their accuracies as labels improves the downstream architecture search efficiency. To explain this finding, we visualize how unsupervised architecture representation learning better encourages neural architectures with similar connections and operators to cluster together. This helps map neural architectures with similar performance to the same regions in the latent space and makes the transition of architectures in the latent space relatively smooth, which considerably benefits diverse downstream search strategies.
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- Award ID(s):
- 1814551
- PAR ID:
- 10295582
- Publisher / Repository:
- Curran Associates Inc.
- Date Published:
- Journal Name:
- Conference on Neural Information Processing Systems
- ISBN:
- 9781713829546
- Format(s):
- Medium: X
- Location:
- Vancouver, Canada.
- Sponsoring Org:
- National Science Foundation
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