Papers with Code 아카이브
Neural Architecture Search
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 26
- 보존된 평가 기록
- 424
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/seriousssam/deep-image-prior-transfer
https://github.com/setharram/facenet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/seulkiyeom/once-for-all
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/seunghwan1228/CNN_EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/seungjaeryanlee/osim-rl-helper
https://github.com/seungwonpark/RandWireNN
Exploring Randomly Wired Neural Networks for Image Recognition
https://github.com/shijianjian/efficientnet-pytorch-3d
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/shiningsunnyday/mcts-chess
https://github.com/showlo/mobilenetv3
https://github.com/shreyesss/PPO-implementation-keras-tensorflow
https://github.com/shuishida/soaprl
https://github.com/siddharthverma314/proximalpolicyoptimization
https://github.com/siddikui/Fast-DARTS
https://github.com/siddikui/ProgressiveDARTS
Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation
https://github.com/silvialuu/DRL-2018
https://github.com/sinamoradian/SNIP-NAS-2
https://github.com/sirakik/mprg_fc
https://github.com/sirakik/ppo_football
https://github.com/sjoshi804/hierarchical-darts-neural-architecture-search
Hierarchical Representations for Efficient Architecture Search
https://github.com/skhetarpal/Prostate_Cancer_Grade_Assessment
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/songhan/DSD
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/songhan/SqueezeNet-Residual
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/stdacore/SinglePathOneShot_MAML
Single Path One-Shot Neural Architecture Search with Uniform Sampling
https://github.com/sunsmarterjie/GOLD_NAS
https://github.com/swdsld/RandWire_tensorflow
Exploring Randomly Wired Neural Networks for Image Recognition
https://github.com/synthlabsai/big-math
https://github.com/szq0214/fkd
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
https://github.com/t-hanya/kuzushiji-recognition
https://github.com/taku-y/20181125-pybullet
https://github.com/takuseno/ppo
https://github.com/tally0818/DARTS
https://github.com/tally0818/NASNet
https://github.com/tarikdgny/Optical_Coherence_Tomography
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/tatsu-lab/linguistic_calibration
https://github.com/tcmxx/CNTKUnityTools
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.