PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search

• URL: http://arxiv.org/abs/2204.12726v1
• Date: Wed, 27 Apr 2022 06:40:39 GMT
• Title: PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Search
• Authors: Yameng Peng, Andy Song, Vic Ciesielski, Haytham M. Fayek, Xiaojun Chang
• Abstract summary: We propose a novel evolutionary-based NAS strategy, Predictor-assisted E-NAS (PRE-NAS) PRE-NAS leverages new evolutionary search strategies and integrates high-fidelity weight inheritance over generations. Experiments on NAS-Bench-201 and DARTS search spaces show that PRE-NAS can outperform state-of-the-art NAS methods.
• Score: 34.06028035262884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural architecture search (NAS) aims to automate architecture engineering in neural networks. This often requires a high computational overhead to evaluate a number of candidate networks from the set of all possible networks in the search space during the search. Prediction of the networks' performance can alleviate this high computational overhead by mitigating the need for evaluating every candidate network. Developing such a predictor typically requires a large number of evaluated architectures which may be difficult to obtain. We address this challenge by proposing a novel evolutionary-based NAS strategy, Predictor-assisted E-NAS (PRE-NAS), which can perform well even with an extremely small number of evaluated architectures. PRE-NAS leverages new evolutionary search strategies and integrates high-fidelity weight inheritance over generations. Unlike one-shot strategies, which may suffer from bias in the evaluation due to weight sharing, offspring candidates in PRE-NAS are topologically homogeneous, which circumvents bias and leads to more accurate predictions. Extensive experiments on NAS-Bench-201 and DARTS search spaces show that PRE-NAS can outperform state-of-the-art NAS methods. With only a single GPU searching for 0.6 days, competitive architecture can be found by PRE-NAS which achieves 2.40% and 24% test error rates on CIFAR-10 and ImageNet respectively.

Related papers

• DNA Family: Boosting Weight-Sharing NAS with Block-Wise Supervisions [121.05720140641189]
  We develop a family of models with the distilling neural architecture (DNA) techniques. Our proposed DNA models can rate all architecture candidates, as opposed to previous works that can only access a sub- search space using algorithms. Our models achieve state-of-the-art top-1 accuracy of 78.9% and 83.6% on ImageNet for a mobile convolutional network and a small vision transformer, respectively.
  arXiv  Detail & Related papers  (2024-03-02T22:16:47Z)
• GeNAS: Neural Architecture Search with Better Generalization [14.92869716323226]
  Recent neural architecture search (NAS) approaches rely on validation loss or accuracy to find the superior network for the target data. In this paper, we investigate a new neural architecture search measure for excavating architectures with better generalization.
  arXiv  Detail & Related papers  (2023-05-15T12:44:54Z)
• A General-Purpose Transferable Predictor for Neural Architecture Search [22.883809911265445]
  We propose a general-purpose neural predictor for Neural Architecture Search (NAS) that can transfer across search spaces. Experimental results on NAS-Bench-101, 201 and 301 demonstrate the efficacy of our scheme.
  arXiv  Detail & Related papers  (2023-02-21T17:28:05Z)
• BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search [100.28980854978768]
  We present Block-wisely Self-supervised Neural Architecture Search (BossNAS) We factorize the search space into blocks and utilize a novel self-supervised training scheme, named ensemble bootstrapping, to train each block separately. We also present HyTra search space, a fabric-like hybrid CNN-transformer search space with searchable down-sampling positions.
  arXiv  Detail & Related papers  (2021-03-23T10:05:58Z)
• Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective [88.39981851247727]
  We propose a novel framework called training-free neural architecture search (TE-NAS) TE-NAS ranks architectures by analyzing the spectrum of the neural tangent kernel (NTK) and the number of linear regions in the input space. We show that: (1) these two measurements imply the trainability and expressivity of a neural network; (2) they strongly correlate with the network's test accuracy.
  arXiv  Detail & Related papers  (2021-02-23T07:50:44Z)
• Weak NAS Predictors Are All You Need [91.11570424233709]
  Recent predictor-based NAS approaches attempt to solve the problem with two key steps: sampling some architecture-performance pairs and fitting a proxy accuracy predictor. We shift the paradigm from finding a complicated predictor that covers the whole architecture space to a set of weaker predictors that progressively move towards the high-performance sub-space. Our method costs fewer samples to find the top-performance architectures on NAS-Bench-101 and NAS-Bench-201, and it achieves the state-of-the-art ImageNet performance on the NASNet search space.
  arXiv  Detail & Related papers  (2021-02-21T01:58:43Z)
• MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search [94.80212602202518]
  We propose Multi-Scale Resource-Aware Neural Architecture Search (MS-RANAS) We employ a one-shot architecture search approach in order to obtain a reduced search cost. We achieve state-of-the-art results in terms of accuracy-speed trade-off.
  arXiv  Detail & Related papers  (2020-09-29T11:56:01Z)
• BRP-NAS: Prediction-based NAS using GCNs [21.765796576990137]
  BRP-NAS is an efficient hardware-aware NAS enabled by an accurate performance predictor-based on graph convolutional network (GCN) We show that our proposed method outperforms all prior methods on NAS-Bench-101 and NAS-Bench-201. We also release LatBench -- a latency dataset of NAS-Bench-201 models running on a broad range of devices.
  arXiv  Detail & Related papers  (2020-07-16T21:58:43Z)
• ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search [6.458169480971417]
  We propose an Architecture-Driven Weight Prediction (ADWP) approach for neural architecture search (NAS) In our approach, we first design an architecture-intensive search space and then train a HyperNetwork by inputting encoding architecture parameters. Results show that one search procedure can be completed in 4.0 GPU hours on CIFAR-10.
  arXiv  Detail & Related papers  (2020-03-03T05:06:20Z)
• DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution Pruning [135.27931587381596]
  We propose an efficient and unified NAS framework termed DDPNAS via dynamic distribution pruning. In particular, we first sample architectures from a joint categorical distribution. Then the search space is dynamically pruned and its distribution is updated every few epochs. With the proposed efficient network generation method, we directly obtain the optimal neural architectures on given constraints.
  arXiv  Detail & Related papers  (2019-05-28T06:35:52Z)

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.