https://github.com/mingxingtan/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional 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/mingxingtan/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/miramind/efficientnets_pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/miroblog/tf_deep_rl_trader
https://github.com/mit-han-lab/ProxylessNAS
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/mit-han-lab/haq
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/mit-han-lab/haq-release
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/mit-han-lab/mcunet
https://github.com/mit-han-lab/torchsparse
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/mit-realm/neuriss
https://github.com/mitchellnw/micro-net-dnw
https://github.com/miv-xjtu/speed
https://github.com/mjpyeon/sedona
https://github.com/mlatsjsu/PDFextract
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
https://github.com/mlvc-lab/Classification-NAS
https://github.com/mnikitin/EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/modelscope/modelscope
https://github.com/montaserFath/Reinforcement-Learning-for-Prosthetics
https://github.com/moon23k/Transformer_Archs
https://github.com/morganmcg1/stanford-cars
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/morikatron/GAIL_PPO
https://github.com/morikatron/PPO
https://github.com/morrisxu-driving/video-music_cross-modal_retrival
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/moukamisama/recon
Automated Search for Resource-Efficient Branched Multi-Task Networks
https://github.com/mr-eggplant/adaxpert0
Single Path One-Shot Neural Architecture Search with Uniform Sampling
https://github.com/mrzzy/np-dl-assign-1
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/msuzen/bristol
https://github.com/mvenouziou/Project-Attention-Is-What-You-Get
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mvpzhangqiu/yolov5prune
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/najlaeLemrabet/FacialKeypointsDetection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/narumiruna/efficientnet-pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/narutomst/Auto-CNN-HSI-Classification
https://github.com/nassimmokhtari/Improved-FireFly-Algorithm
https://github.com/nazarov-yuriy/zh-ru-shared-task
https://github.com/near32/regym
https://github.com/negrinho/deep_architect_legacy
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.