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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/pytorch/vision
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/qubvel/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rajneeshaggarwal/google-efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ravi02512/efficientdet-keras
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ray-project/ray/tree/master/rllib
https://github.com/reinforcement-learning-kr/pg_travel
https://github.com/renmada/MnasNet-paddle
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/researchmm/CyDAS
https://github.com/reyvaz/pneumothorax_detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/reyvaz/steel-defect-segmentation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rishikksh20/LightSpeech
LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search
https://github.com/rmwkwok/product_visual_search
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rohitgr7/tvmodels
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/royson/tpsr
https://github.com/rshnn/battleship
https://github.com/rubinxin/SoTL
Speedy Performance Estimation for Neural Architecture Search
https://github.com/rutgerswiselab/autolossgen
https://github.com/rwbfd/OpenCompetitionV2
Searching for A Robust Neural Architecture in Four GPU Hours
https://github.com/rwightman/efficientnet-jax
https://github.com/rwightman/gen-efficientnet-pytorch
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/rwightman/genmobilenet-pytorch
https://github.com/rwightman/pytorch-image-models
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/s-sd/task-amenability
https://github.com/samuellundberg/NeuralArchitectureSearch
https://github.com/sandipsahajoy/Predicting-Lymphoma-using-CNN-in-Keras
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/saschaschramm/Pong
https://github.com/sayakpaul/swin-transformers-tf
https://github.com/sc2crazy/StarCrackRL
https://github.com/scenarios/PGNAS
https://github.com/schoolboy-ju/Proxyless-NAS
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
https://github.com/scz233/darts
https://github.com/sdxzy/LPM-PyTorch-implementation
https://github.com/seermer/TensorFlow2-EfficientNetV2
https://github.com/semskurto/APTOS
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/sense-x/tokenmix
UniNet: Unified Architecture Search with Convolution, Transformer, and MLP
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.