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/hyang0129/foodclassapp
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hyeonjeongha/mm-poisonrag
Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations
https://github.com/iamilyasedunov/key_word_spotting
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/iamkanghyunchoi/ait
AutoReCon: Neural Architecture Search-based Reconstruction for Data-free Compression
https://github.com/idealo/imagededup
https://github.com/idstcv/ZenNAS
https://github.com/ifestus/rl
https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail
https://github.com/ikostrikov/pytorch-rl
https://github.com/inacmor/mobiledets-yolov4-pytorch
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
https://github.com/innvariant/deepstruct
https://github.com/inoryy/reaver
https://github.com/intellabs/model-compression-research-package
https://github.com/intellisys-lab/stellaris-sc24
https://github.com/invisibleForce/ENAS-Pytorch
https://github.com/isaachaw/GrabCarRecognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/itouchz/Neural-Predictor-Tensorflow
https://github.com/jafermarq/WinogradAwareNets
https://github.com/jahongir7174/EffcientNetV2
https://github.com/jahongir7174/EfficientNetV2
https://github.com/jaiwei98/MobileNetV4-pytorch
https://github.com/jaketae/mlp-mixer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/james77777778/keras-image-models
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/jaminfong/fna
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search
https://github.com/jason90330/EdgeFinal
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/jaygala24/lfm-formulation-1
Learning from Mistakes -- A Framework for Neural Architecture Search
https://github.com/jcwleo/curiosity-driven-exploration-pytorch
https://github.com/jcwleo/random-network-distillation-pytorch
https://github.com/jerry73204/mobilenet-v3-rs
https://github.com/jfpettit/flare
https://github.com/jhare96/reinforcement-learning
https://github.com/jiangyingjunn/i-darts
https://github.com/jianzhnie/AutoTabular
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
https://github.com/jiaowoguanren0615/MobileNetV4
https://github.com/jim-schwoebel/allie
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.