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/jmjeon94/MobileNet-Pytorch
https://github.com/joe-siyuan-qiao/NeuralRejuvenation-CVPR19
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization
https://github.com/johannesu/NASNet-keras
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/jongornet14/HyperController
https://github.com/jordan-hs/diversity_is_definitely_needed
Once-for-All: Train One Network and Specialize it for Efficient Deployment
https://github.com/jordan-hs/rss-interference-cvprw2022
Once-for-All: Train One Network and Specialize it for Efficient Deployment
https://github.com/js-aguiar/wheat-object-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/jsztompka/MultiAgent-PPO
https://github.com/jsztompka/PPO-demo
https://github.com/juliusfrost/Research-Paper-Implementations
https://github.com/junaidaliop/MobileNetV4
https://github.com/jvidals09/Decentralized-and-multi-agent-control-of-Franka-Emika-Panda-robot-in-continuous-task-execution
https://github.com/jw1401/PPO-Tensorflow-2.0
https://github.com/kaileymonn/Quantized-ENAS-ConvNets
https://github.com/kairess/efficientnet_example
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/kc-ml2/darts
https://github.com/kdha0727/lung-opacity-and-covid-chest-x-ray-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/keivanalizadeh/ButterflyTransform
Butterfly Transform: An Efficient FFT Based Neural Architecture Design
https://github.com/keonlee9420/DiffSinger
LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search
https://github.com/keras-team/autokeras
https://github.com/khanrc/pt.darts
https://github.com/kingcong/ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/kingcong/gpu_ISyNet
ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/kmdanielduan/TransNASBench
https://github.com/kmz4/QHACK2021
https://github.com/konstantinos-p/image_classification_SOTA
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ku2482/gail-airl-ppo.pytorch
https://github.com/kuan-wang/pytorch-mobilenet-v3
https://github.com/ky941122/darts-experiment
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/lamda-bbo/offline-moo
https://github.com/laoshiwei/face-anti-spoofing
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
https://github.com/lars-research/f2gnn
Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective
https://github.com/lars-research/pas-ogb
https://github.com/lcswillems/torch-ac
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.