Neural Architecture Search
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Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures.
Image Credit : NAS with Reinforcement Learning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Mind23-2/MindCode-101/tree/main/nasnet
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
https://github.com/Mind23-2/MindCode-102
https://github.com/Mind23-2/MindCode-105
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/Mind23-2/MindCode-107
https://github.com/Mind23-2/MindCode-26
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/Mind23-2/MindCode-3/tree/main/HourNAS
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
https://github.com/Mind23-2/MindCode-3/tree/main/ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/Mind23-2/MindCode-3/tree/main/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/Mind23-2/MindCode-3/tree/main/mobilenetv3_large
https://github.com/Mind23-2/MindCode-3/tree/main/nas-fpn
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
https://github.com/Mind23-2/MindCode-3/tree/main/nasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/Mind23-2/MindCode-37
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Mind23-2/MindCode-5/tree/main/PDarts
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
https://github.com/Mind23-2/MindCode-5/tree/main/pnasnet
https://github.com/Mind23-2/MindCode-5/tree/main/proxylessnas
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
https://github.com/Mind23-2/MindCode-58
https://github.com/Mind23-2/MindCode-64
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/Mind23-2/MindCode-7
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/Mind23-2/MindCode-88/tree/main/single_path_nas
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/MindCode-4/code-10/tree/main/GraphPAS
GraphPAS: Parallel Architecture Search for Graph Neural Networks
https://github.com/MindCode-4/code-14/tree/main/GraphPAS
GraphPAS: Parallel Architecture Search for Graph Neural Networks
https://github.com/MindSpore-MS-Code2/code0/tree/main/dpn
DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy
https://github.com/MindSpore-paper-code-2/code2/tree/main/PDarts
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild
https://github.com/MindSpore-paper-code-2/code2/tree/main/proxylessnas
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_resnet_34
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/MindSpore-paper-code-2/code400/tree/main/Efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code1/tree/main/ArbitraryStyleTransfer
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/MindSpore-paper-code-3/code1/tree/main/Auto-DeepLab
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search
https://github.com/MindSpore-paper-code-3/code1/tree/main/AutoSlim
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
https://github.com/MindSpore-paper-code-3/code2/tree/main/darknet53
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet50_bam
https://github.com/MindSpore-paper-code-3/code6/tree/main/Auto-DeepLab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/MindSpore-paper-code-3/code6/tree/main/AutoSlim
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
https://github.com/MindSpore-paper-code-3/code7/tree/main/FCOS
NAS-FCOS: Fast Neural Architecture Search for Object Detection
https://github.com/MindSpore-paper-code-3/code8/tree/main/darknet53
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.