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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/tensorflow/models/tree/master/research/deeplab
https://github.com/tensorflow/models/tree/master/research/slim
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/tensorflow/tpu/tree/master/models/official/amoeba_net
Regularized Evolution for Image Classifier Architecture Search
https://github.com/tensorflow/tpu/tree/master/models/official/detection
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/tensorlayer/RLzoo
https://github.com/tetette/Bachelorthesis
https://github.com/theresearchai/vehicle_routing_rl_2
https://github.com/tiagoCuervo/JapaNet
https://github.com/tianbaochou/CNAS
https://github.com/tiangexiang/BiX-NAS
BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation
https://github.com/tidedra/vl-rlhf
https://github.com/tilkb/thermoai
https://github.com/timctho/random-wired-nn-tensorflow
Exploring Randomly Wired Neural Networks for Image Recognition
https://github.com/tinhb92/rnn_darts_fastai
https://github.com/titu1994/keras-efficientnets
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/titu1994/neural-architecture-search
https://github.com/titu1994/progressive-neural-architecture-search
https://github.com/tmjeong1103/RL_with_RAY
https://github.com/toanbkmt/EfficientnetFruitDetect
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/tobiasemrich/SchafkopfRL
https://github.com/tommyvsfu1/RL-NTU
https://github.com/toni-sm/skrl
https://github.com/topdu/openocr
AutoSTR: Efficient Backbone Search for Scene Text Recognition
https://github.com/towhee-io/towhee
MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/triple7/Keras-WGAN-RGB-128x128
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/tsing-cv/EfficientNet-tensorflow-eager
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ttruty/facial-feature-mouse-control
https://github.com/tuanpnm99/RLPongAgent
https://github.com/tuggeluk/pycls
https://github.com/twice154/ofa-for-super-resolution
Once-for-All: Train One Network and Specialize it for Efficient Deployment
https://github.com/uber-research/GTN
https://github.com/uber-research/Synthetic-Petri-Dish
Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search
https://github.com/ujsyehao/mobilenetv3-ssd
https://github.com/ultmaster/neuralpredictor.pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.