https://github.com/ambroggi/pruning-project-for-deep-neural-networks
DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator 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/amirdy/dog-breed-classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/amzoyang/CS-221-Final-Project
https://github.com/analogdevicesinc/ai8x-training
Once-for-All: Train One Network and Specialize it for Efficient Deployment
https://github.com/andyljones/zonotable
https://github.com/anonDEHB/DEHB
Regularized Evolution for Image Classifier Architecture Search
https://github.com/anonNo2/MulTeacher-KD
https://github.com/anonymousest/DBSN
Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure
https://github.com/anorthman/custom
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
https://github.com/anthonysong98/super-mario-bros-ppo
https://github.com/antonFJohansson/Bayesian-Learning-of-Neural-Network-Architectures
https://github.com/araffin/sbx
https://github.com/aragakiyuiii/Gumbel-darts-master
https://github.com/aragakiyuiii/gumbel-pdarts-master
Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation
https://github.com/arberzela/EfficientNAS
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search
https://github.com/ardeal/EfficientNetV2
https://github.com/armin-azh/3DefficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/arnomoonens/yarll
https://github.com/arturjordao/arturjordao
https://github.com/asad-62/IVP-DNN
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/ashaw596/squeezenas
SqueezeNAS: Fast neural architecture search for faster semantic segmentation
https://github.com/atregret/mobilenetv3
https://github.com/attaullah/Pretraining-WideResNet
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
https://github.com/autogluon/autogluon
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
https://github.com/automl-4paradigm/snag
https://github.com/automl-research/diffmg
DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks
https://github.com/automl-research/snag
https://github.com/automl/EfficientNAS
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search
https://github.com/automl/learna
https://github.com/automl/neps
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars
https://github.com/aws-samples/autogluon-on-aws
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
https://github.com/awslabs/autogluon
https://github.com/aymanshams07/enas_cifar10
https://github.com/azamatkhid/mnasnet-pytorch
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/baiyancheng20/gaga
https://github.com/barisozmen/deepaugment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.