https://github.com/megvii-model/AngleNAS
Angle-based Search Space Shrinking for Neural Architecture Search
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/megvii-model/SinglePathOneShot
Single Path One-Shot Neural Architecture Search with Uniform Sampling
https://github.com/megvii-research/basecls/tree/main/zoo/public/effnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/meijieru/yet_another_mobilenet_series
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/melodyguan/enas
https://github.com/michael-snower/ppo
https://github.com/microsoft/NeuralSpeech/tree/master/LightSpeech
LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search
https://github.com/microsoft/moonlit
SpaceEvo: Hardware-Friendly Search Space Design for Efficient INT8 Inference
https://github.com/microsoft/nn-meter
Random Search and Reproducibility for Neural Architecture Search
https://github.com/microsoft/only_train_once
https://github.com/microsoft/strategically_efficient_rl
https://github.com/midasklr/yolov5prune
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/mightypirate1/PPO_homebrew
https://github.com/mindspore-ai/models/blob/master/research/cv/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mindspore-ai/models/blob/master/research/cv/efficientnetv2
https://github.com/mindspore-ai/models/blob/master/research/cv/mnasnet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/mindspore-ai/models/blob/master/research/cv/mobilenetv3_large
https://github.com/mindspore-ai/models/tree/master/official/cv/nasnet
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/mindspore-ai/models/tree/master/research/cv/Auto-DeepLab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/mindspore-ai/models/tree/master/research/cv/HourNAS
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
https://github.com/mindspore-ai/models/tree/master/research/cv/PDarts
Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation
https://github.com/mindspore-ai/models/tree/master/research/cv/adelaide_ea
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
https://github.com/mindspore-ai/models/tree/master/research/cv/nas-fpn
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
https://github.com/mindspore-ai/models/tree/master/research/cv/pnasnet
https://github.com/mindspore-ai/models/tree/master/research/cv/proxylessnas
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
https://github.com/mindspore-ai/models/tree/master/research/cv/renas
https://github.com/mindspore-ai/models/tree/master/research/cv/resnet50_bam
https://github.com/mindspore-ai/models/tree/master/research/cv/single_path_nas
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/mindspore-courses/Deep-Reinforcement-Learning-Algorithms-with-MindSpore
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/BAM.py
https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/conv/MBConv.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/efficientnet.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/mnasnet.py
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/mobilenet_v3.py
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/nasnet.py
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/pnasnet.py
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.