GEA - Code for Guided Evolution for Neural Architecture Search

Related tags

Deep Learning GEA
Overview

Efficient Guided Evolution for Neural Architecture Search

Usage

Create a conda environment using the env.yml file

conda env create -f env.yml

Activate the environment and follow the instructions to install

conda activate gea

Install nasbench (see https://github.com/google-research/nasbench)

Download the NDS data from https://github.com/facebookresearch/nds and place the json files in path_to_code/nds_data/ Download the NASbench101 data (see https://github.com/google-research/nasbench) Download the NASbench201 data (see https://github.com/D-X-Y/NAS-Bench-201)

Reproduce all of the results by running

./run.sh

The code is licensed under the MIT licence.

Acknowledgements

This repository makes liberal use of code from the AutoDL library, NAS-Bench-201, NAS-Bench-101 and NAS-WOT. We are grateful to the authors for making the implementations publicly available.

Citing us

If you use or build on our work, please consider citing us:

@inproceedings{gea2021,
    title={Guided Evolution for Neural Architecture Search},
    author={Vasco Lopes and Miguel Santos and Bruno Degardin and Luís A. Alexandre},
    year={2021},
    booktitle={Advances in Neural Information Processing Systems 35 (NeurIPS) - New In ML}
}
You might also like...
[ICLR 2021] HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark
[ICLR 2021] HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark

HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark Accepted as a spotlight paper at ICLR 2021. Table of content File structure Prerequi

BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search

BossNAS This repository contains PyTorch evaluation code, retraining code and pretrained models of our paper: BossNAS: Exploring Hybrid CNN-transforme

Deep Multimodal Neural Architecture Search
Deep Multimodal Neural Architecture Search

MMNas: Deep Multimodal Neural Architecture Search This repository corresponds to the PyTorch implementation of the MMnas for visual question answering

Official implementation of  Rethinking Graph Neural Architecture Search from Message-passing (CVPR2021)
Official implementation of Rethinking Graph Neural Architecture Search from Message-passing (CVPR2021)

Rethinking Graph Neural Architecture Search from Message-passing Intro The GNAS can automatically learn better architecture with the optimal depth of

Block-wisely Supervised Neural Architecture Search with Knowledge Distillation (CVPR 2020)
Block-wisely Supervised Neural Architecture Search with Knowledge Distillation (CVPR 2020)

DNA This repository provides the code of our paper: Blockwisely Supervised Neural Architecture Search with Knowledge Distillation. Illustration of DNA

"NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search".

NAS-Bench-301 This repository containts code for the paper: "NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search". The

[CVPR21] LightTrack: Finding Lightweight Neural Network for Object Tracking via One-Shot Architecture Search
[CVPR21] LightTrack: Finding Lightweight Neural Network for Object Tracking via One-Shot Architecture Search

LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search The official implementation of the paper LightTra

Official PyTorch implementation of
Official PyTorch implementation of "Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets" (ICLR 2021)

Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets This is the official PyTorch implementation for the paper Rapid Neural A

 Few-shot Neural Architecture Search
Few-shot Neural Architecture Search

One-shot Neural Architecture Search uses a single supernet to approximate the performance each architecture. However, this performance estimation is super inaccurate because of co-adaption among operations in supernet.

Owner
null
Densely Connected Search Space for More Flexible Neural Architecture Search (CVPR2020)

DenseNAS The code of the CVPR2020 paper Densely Connected Search Space for More Flexible Neural Architecture Search. Neural architecture search (NAS)

Jamin Fong 291 Nov 18, 2022
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

What is DeepHyper? DeepHyper is a software package that uses learning, optimization, and parallel computing to automate the design and development of

DeepHyper Team 214 Jan 8, 2023
Model search is a framework that implements AutoML algorithms for model architecture search at scale

Model search (MS) is a framework that implements AutoML algorithms for model architecture search at scale. It aims to help researchers speed up their exploration process for finding the right model architecture for their classification problems (i.e., DNNs with different types of layers).

Google 3.2k Dec 31, 2022
Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)'

SCL Introduction Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)' We evaluated our approach using two baseline

null 34 Oct 8, 2022
Code release to accompany paper "Geometry-Aware Gradient Algorithms for Neural Architecture Search."

Geometry-Aware Gradient Algorithms for Neural Architecture Search This repository contains the code required to run the experiments for the DARTS sear

null 18 May 27, 2022
code for "AttentiveNAS Improving Neural Architecture Search via Attentive Sampling"

code for "AttentiveNAS Improving Neural Architecture Search via Attentive Sampling"

Facebook Research 94 Oct 26, 2022
Code repository accompanying the paper "On Adversarial Robustness: A Neural Architecture Search perspective"

On Adversarial Robustness: A Neural Architecture Search perspective Preparation: Clone the repository: https://github.com/tdchaitanya/nas-robustness.g

Chaitanya Devaguptapu 4 Nov 10, 2022
NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

Göktuğ Karakaşlı 16 Dec 5, 2022
Deep Image Search is an AI-based image search engine that includes deep transfor learning features Extraction and tree-based vectorized search.

Deep Image Search - AI-Based Image Search Engine Deep Image Search is an AI-based image search engine that includes deep transfer learning features Ex

null 139 Jan 1, 2023
[ICLR 2021] "Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective" by Wuyang Chen, Xinyu Gong, Zhangyang Wang

Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective [PDF] Wuyang Chen, Xinyu Gong, Zhangyang Wang In ICLR 2

VITA 156 Nov 28, 2022