Most existing neural architecture search (NAS) benchmarks and algorithms prioritize well-studied tasks, eg image classification on CIFAR or ImageNet. This makes the performance of NAS approaches in more diverse areas poorly understood. In this paper, we present NAS-Bench-360, a benchmark suite to evaluate methods on domains beyond those traditionally studied in architecture search, and use it to address the following question: do state-of-the-art NAS methods perform well on diverse tasks? To construct the benchmark, we curate ten tasks spanning a diverse array of application domains, dataset sizes, problem dimensionalities, and learning objectives. Each task is carefully chosen to interoperate with modern CNN-based search methods while possibly being far-afield from its original development domain. To speed up and reduce the cost of NAS research, for two of the tasks we release the precomputed performance of 15,625 architectures comprising a standard CNN search space. Experimentally, we show the need for more robust NAS evaluation of the kind NAS-Bench-360 enables by showing that several modern NAS procedures perform inconsistently across the ten tasks, with many catastrophically poor results. We also demonstrate how NAS-Bench-360 and its associated precomputed results will enable future scientific discoveries by testing whether several recent hypotheses promoted in the NAS literature hold on diverse tasks. NAS-Bench-360 is hosted at https://nb360. ml. cmu. edu.
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Network Compression via Cooperative Architecture Search and Distillation
Neural Architecture Search (NAS) and its variants
are competitive in many computer vision tasks lately. In this
paper, we develop a Cooperative Architecture Search and Distillation (CASD) method for network compression. Compared with prior art, our method achieves better performance in ResNet-164 pruning on CIFAR-10 and CIFAR-100 image classifications, promising to be extended to other tasks.
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- NSF-PAR ID:
- 10335367
- Date Published:
- Journal Name:
- Proceedings IEEE International Conference on Artificial Intelligence for Industries
- ISSN:
- 2770-4718
- Page Range / eLocation ID:
- 42-43
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/DOI 10.1109/AI4I51902.2021.00018
