https://gitee.com/ncucjm/GraphPAS
GraphPAS: Parallel Architecture Search for Graph Neural Networks
Papers with Code 아카이브
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/0h-n0/tfdbonas
Deep Neural Architecture Search with Deep Graph Bayesian Optimization
https://github.com/0h-n0/thdbonas
Deep Neural Architecture Search with Deep Graph Bayesian Optimization
https://github.com/170928/-Review-Generative-Adversarial-Imitation-Learning
https://github.com/170928/-Review-Proximal-Policy-Optimization-Algorithms
https://github.com/2023-MindSpore-1/ms-code-1
BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search
https://github.com/2023-MindSpore-1/ms-code-151
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/Auto-DeepLab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/adelaide_ea
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
https://github.com/2023-MindSpore-1/ms-code-17/tree/main/AutoSlim
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
https://github.com/2023-MindSpore-1/ms-code-18/tree/main/ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/2023-MindSpore-1/ms-code-185
https://github.com/2023-MindSpore-1/ms-code-189/tree/main/modelarts
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-193
https://github.com/2023-MindSpore-1/ms-code-195
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2023-MindSpore-1/ms-code-21
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/HourNAS
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/PGAN
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pnasnet
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/proxylessnas
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnet50_bam
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/sr_ea
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
https://github.com/2023-MindSpore-1/ms-code-224/tree/main/adelaide_ea
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
https://github.com/2023-MindSpore-4/Code-5/tree/main/HourNAS
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
https://github.com/2023-MindSpore-4/Code-5/tree/main/ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/2023-MindSpore-4/Code-5/tree/main/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/2023-MindSpore-4/Code-5/tree/main/mobilenetV3_small_x1_0
https://github.com/2023-MindSpore-4/Code-5/tree/main/mobilenetv3_large
https://github.com/2023-MindSpore-4/Code-5/tree/main/nas-fpn
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
https://github.com/2023-MindSpore-4/Code11/tree/main/retinanet_resnet152
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code11/tree/main/single_path_nas
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/2023-MindSpore-4/Code11/tree/main/sr_ea
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
https://github.com/2023-MindSpore-4/Code14/tree/main/SpineNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code14/tree/main/resnet50_bam
https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet101
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2023-MindSpore-4/Code14/tree/main/retinanet_resnet152
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.