https://github.com/GAIA-vision/GAIA-cv
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models
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/GiadaSimionato/Reasoning_Agents_2020
https://github.com/GiuliaLanzillotta/INAS
https://github.com/GoodAI/GTN
https://github.com/Gouet/Acrobot-PPO
https://github.com/Gouet/Breakout-V0
https://github.com/Gouet/PPO-gym
https://github.com/Gouet/PPO-pytorch
https://github.com/Gregory-Eales/proximal-policy-optimization
https://github.com/Guang000/Awesome-Dataset-Distillation/blob/main/README.md
https://github.com/HO4X/TSR_JetsonTX2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/HaiTMai/Time-Series-Forecast
https://github.com/HaoyueBaiZJU/NAS-OoD
NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization
https://github.com/HeungChangLee/AutoML
https://github.com/HyeonhoonLee/MAIC2021_Sleep
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/IMvision12/keras-vision-models
https://github.com/IRES-FAU/Evolutionary-Architecture-Search-for-Graph-Neural-Networks
https://github.com/InSpaceAI/RL-Zoo
https://github.com/Insaniauser/ViPNAS_mindspore
ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search
https://github.com/Ipsedo/EvoMotion
https://github.com/IsmaelCesar/darts
https://github.com/JL321/Proximal-Policy-Optimization
https://github.com/JacobM184/EfficientNet-for-Gun-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/JeongwookUm/TEST_AutoSpeech-master
AutoSpeech: Neural Architecture Search for Speaker Recognition
https://github.com/JiaminRen/RandWireNN
Exploring Randomly Wired Neural Networks for Image Recognition
https://github.com/JihaoLee/Randomly_Wired_reproducibility
Exploring Randomly Wired Neural Networks for Image Recognition
https://github.com/Jintao-Huang/EfficientNet_PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Jmak12/Iris1
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/JoegameZhou/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/JonasRSV/PPO
https://github.com/JunrQ/NAS
https://github.com/KXY-PUBLIC/NASE
NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search
https://github.com/Khrylx/PyTorch-RL
https://github.com/KlabCMU/ESNAC
Learnable Embedding Space for Efficient Neural Architecture Compression
https://github.com/Klassikcat/project-NEXTLab-CNN-EfficientNet
https://github.com/KnightZhang625/DARTS_NAS_TextClassification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.