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/S4iz/beta
https://github.com/SAGNIKMJR/CODEBRIM_MetaQNN
https://github.com/SLDGroup/NN_Mass
https://github.com/SPark9625/PyTorch-Proximal-Policy-Optimization
https://github.com/SaharR1372/DM_Style_matching
https://github.com/Sakib1263/MobileNet-1D-2D-Tensorflow-Keras
https://github.com/SalvatoreCognetta/reasoning-agent-project
https://github.com/ShunLu91/DU-DARTS
https://github.com/ShunLu91/Single-Path-One-Shot-NAS
Single Path One-Shot Neural Architecture Search with Uniform Sampling
https://github.com/SifatMd/Research-Papers
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/SourangshuGhosh/CGP-CNN-Design
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
https://github.com/SpikeKing/mobilenet_v3
https://github.com/StevenGerrad/large-scale-Evolution-Net
https://github.com/Stippler/cow-simulator
https://github.com/SunDoge/efficientnet-pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Syavaprd/mobilenet_v3
https://github.com/TabishA/cgpunet
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
https://github.com/TezRomacH/layer-to-layer-pytorch
Training Large Neural Networks with Constant Memory using a New Execution Algorithm
https://github.com/TheConstant3/MobileNetV3-Keras
https://github.com/Tingelam/tianchi_gd_defect_pytorch_round1
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/Tirth27/Skin-Cancer-Classification-using-Deep-Learning
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/TranNhiem/Neural_Network_Architecture_Search
$μ$NAS: Constrained Neural Architecture Search for Microcontrollers
https://github.com/TravisLeeTS/grabcvchallenge
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/TreeLimes/QANAS
https://github.com/UTS-AAi/autoweka
AutonoML: Towards an Integrated Framework for Autonomous Machine Learning
https://github.com/UesugiErii/tf2-PPO-atari
https://github.com/VITA-Group/AutoGAN
AutoGAN: Neural Architecture Search for Generative Adversarial Networks
https://github.com/VITA-Group/AutoSpeech
AutoSpeech: Neural Architecture Search for Speaker Recognition
https://github.com/VainF/Torch-Pruning
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/VinayBhupalam/melanoma-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/WillButAgain/ENAS
https://github.com/WonJunPark/Efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/XinbangZhang/DSO-NAS
You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization
https://github.com/YaCpotato/B4ResearchDeepaugment
https://github.com/YaCpotato/deepaugmentFix
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.