https://github.com/code-implementation1/Code6/tree/main/nasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
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/code-implementation1/Code6/tree/main/pnasnet
https://github.com/code-implementation1/Code6/tree/main/proxylessnas
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
https://github.com/code-implementation1/Code7/tree/main/resnet50_bam
https://github.com/code-implementation1/Code7/tree/main/retinanet_resnet101
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/code-implementation1/Code8/tree/main/SpineNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/code-implementation1/Code8/tree/main/single_path_nas
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/countif/enas_nni
https://github.com/cshannonn/blackscholes_nas
https://github.com/curryandsun/AIOL
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
https://github.com/cvtower/SeesawNet-pytorch-reimplement
Seesaw-Net: Convolution Neural Network With Uneven Group Convolution
https://github.com/cvtower/SeesawNet_pytorch
Seesaw-Net: Convolution Neural Network With Uneven Group Convolution
https://github.com/cyrilminaeff/MobileNet
https://github.com/d-li14/efficientnetv2.pytorch
https://github.com/d-li14/mobilenetv3.pytorch
https://github.com/daekeun-ml/autogluon-on-aws
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
https://github.com/danelee2601/rl-based-automatic-berthing
https://github.com/danielpatrickhug/Research_Paper_Parser
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/danstowell/insect_classifier_GDSC23_insecteffnet
https://github.com/darya-baranovskaya/keyword_spotting
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/daya6489/DriveML
https://github.com/ddghost/new_darts
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
https://github.com/decoderkurt/research_project_school_of_ai_2019
https://github.com/deconlabs/Binanace-trading-simulation
https://github.com/deconlabs/Binanace_trading_simulation
https://github.com/deconlabs/TradingZoo-Dynamic-fee-simulation
https://github.com/denizyuret/playground
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/diasirish/mobilenetv3
https://github.com/dicarlolab/archconvnets
https://github.com/dickreuter/neuron_poker
https://github.com/diff7/DARTS-devices
https://github.com/distrue/enas_tensorflow
https://github.com/dmiu-shell/deeprl-shell
https://github.com/dmlc/gluon-cv
https://github.com/donamin/llc
https://github.com/downingbots/RLDonkeycar
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.