Accelerating Neural Architecture Search via Proxy Data
- URL: http://arxiv.org/abs/2106.04784v1
- Date: Wed, 9 Jun 2021 03:08:53 GMT
- Title: Accelerating Neural Architecture Search via Proxy Data
- Authors: Byunggook Na, Jisoo Mok, Hyeokjun Choe, Sungroh Yoon
- Abstract summary: We propose a novel proxy data selection method tailored for neural architecture search (NAS)
executing DARTS with the proposed selection requires only 40 minutes on CIFAR-10 and 7.5 hours on ImageNet with a single GPU.
When the architecture searched on ImageNet using the proposed selection is inversely transferred to CIFAR-10, a state-of-the-art test error of 2.4% is yielded.
- Score: 17.86463546971522
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Despite the increasing interest in neural architecture search (NAS), the
significant computational cost of NAS is a hindrance to researchers. Hence, we
propose to reduce the cost of NAS using proxy data, i.e., a representative
subset of the target data, without sacrificing search performance. Even though
data selection has been used across various fields, our evaluation of existing
selection methods for NAS algorithms offered by NAS-Bench-1shot1 reveals that
they are not always appropriate for NAS and a new selection method is
necessary. By analyzing proxy data constructed using various selection methods
through data entropy, we propose a novel proxy data selection method tailored
for NAS. To empirically demonstrate the effectiveness, we conduct thorough
experiments across diverse datasets, search spaces, and NAS algorithms.
Consequently, NAS algorithms with the proposed selection discover architectures
that are competitive with those obtained using the entire dataset. It
significantly reduces the search cost: executing DARTS with the proposed
selection requires only 40 minutes on CIFAR-10 and 7.5 hours on ImageNet with a
single GPU. Additionally, when the architecture searched on ImageNet using the
proposed selection is inversely transferred to CIFAR-10, a state-of-the-art
test error of 2.4\% is yielded. Our code is available at
https://github.com/nabk89/NAS-with-Proxy-data.
Related papers
- Meta-prediction Model for Distillation-Aware NAS on Unseen Datasets [55.2118691522524]
Distillation-aware Neural Architecture Search (DaNAS) aims to search for an optimal student architecture.
We propose a distillation-aware meta accuracy prediction model, DaSS (Distillation-aware Student Search), which can predict a given architecture's final performances on a dataset.
arXiv Detail & Related papers (2023-05-26T14:00:35Z) - Speeding up NAS with Adaptive Subset Selection [21.31075249079979]
We present an adaptive subset selection approach to neural architecture search (NAS)
We devise an algorithm that makes use of state-of-the-art techniques from both areas.
Our results are consistent across multiple datasets.
arXiv Detail & Related papers (2022-11-02T19:48:42Z) - UnrealNAS: Can We Search Neural Architectures with Unreal Data? [84.78460976605425]
Neural architecture search (NAS) has shown great success in the automatic design of deep neural networks (DNNs)
Previous work has analyzed the necessity of having ground-truth labels in NAS and inspired broad interest.
We take a further step to question whether real data is necessary for NAS to be effective.
arXiv Detail & Related papers (2022-05-04T16:30:26Z) - OPANAS: One-Shot Path Aggregation Network Architecture Search for Object
Detection [82.04372532783931]
Recently, neural architecture search (NAS) has been exploited to design feature pyramid networks (FPNs)
We propose a novel One-Shot Path Aggregation Network Architecture Search (OPANAS) algorithm, which significantly improves both searching efficiency and detection accuracy.
arXiv Detail & Related papers (2021-03-08T01:48:53Z) - NASTransfer: Analyzing Architecture Transferability in Large Scale
Neural Architecture Search [18.77097100500467]
Neural Architecture Search (NAS) is an open and challenging problem in machine learning.
The typical way of conducting large scale NAS is to search for an architectural building block on a small dataset and then transfer the block to a larger dataset.
We analyze the architecture transferability of different NAS methods by performing a series of experiments on large scale benchmarks such as ImageNet1K and ImageNet22K.
arXiv Detail & Related papers (2020-06-23T20:28:42Z) - DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search [76.9225014200746]
Efficient search is a core issue in Neural Architecture Search (NAS)
We present DA-NAS that can directly search the architecture for large-scale target tasks while allowing a large candidate set in a more efficient manner.
It is 2x faster than previous methods while the accuracy is currently state-of-the-art, at 76.2% under small FLOPs constraint.
arXiv Detail & Related papers (2020-03-27T17:55:21Z) - DSNAS: Direct Neural Architecture Search without Parameter Retraining [112.02966105995641]
We propose a new problem definition for NAS, task-specific end-to-end, based on this observation.
We propose DSNAS, an efficient differentiable NAS framework that simultaneously optimize architecture and parameters with a low-biased Monte Carlo estimate.
DSNAS successfully discovers networks with comparable accuracy (74.4%) on ImageNet in 420 GPU hours, reducing the total time by more than 34%.
arXiv Detail & Related papers (2020-02-21T04:41:47Z) - NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture
Search [55.12928953187342]
We propose an extension to NAS-Bench-101: NAS-Bench-201 with a different search space, results on multiple datasets, and more diagnostic information.
NAS-Bench-201 has a fixed search space and provides a unified benchmark for almost any up-to-date NAS algorithms.
We provide additional diagnostic information such as fine-grained loss and accuracy, which can give inspirations to new designs of NAS algorithms.
arXiv Detail & Related papers (2020-01-02T05:28:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.