https://github.com/ultralytics/yolov5
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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/uts-caslab/autoweka
AutonoML: Towards an Integrated Framework for Autonomous Machine Learning
https://github.com/uvipen/super-mario-bros-ppo-pytorch
https://github.com/vcadillog/PPO-Mario-Bros-Tensorflow-2
https://github.com/veegee82/DARTS
https://github.com/vermashresth/damage-aware-PPO
https://github.com/vheuthe/microbot_rl
https://github.com/victoriaqiu/10707finalproject
https://github.com/vladthesav/MoldAI
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/voqtuyen/CDCN-Face-Anti-Spoofing.pytorch
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
https://github.com/wackygerbs/NAS-DIP
https://github.com/wang-zidu/3ddfa-v3
https://github.com/wangcn0201/MoARR
Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation
https://github.com/wangshub/RL-Stock
https://github.com/wangyi111/international-archaeology-ai-challenge
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/wangzhengfei0730/NIPS2018-AIforProsthetics
https://github.com/weiwenjiang/hotNAS-CODES20
Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start
https://github.com/wh1090220084/mobilenetv4_pytorch
https://github.com/wolszhang/randWireNN
Exploring Randomly Wired Neural Networks for Image Recognition
https://github.com/wusaifei/HWCC_image_classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/xiangning-chen/SIF
https://github.com/xiaochus/MobileNetV3
https://github.com/xiaoiker/GCN-NAS
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching
https://github.com/xiaolai-sqlai/mobilenetv3
https://github.com/xiaomi-automl/FALSR
Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
https://github.com/xiaomi-automl/FairNAS
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search
https://github.com/xiaoshingshing/ReNAS
https://github.com/xiawenwen49/ppo
https://github.com/xingchenwan/nasbowl
Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels
https://github.com/xiuyu0000/new_papers_codes/tree/main/ppo
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/backbones/efficientnet.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/backbones/mobilenet_v3.py
https://github.com/xkp793003821/PC-DARTS-COOPER
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
https://github.com/xkp793003821/autokeras_cooper
https://github.com/xmu-xiaoma666/External-Attention-pytorch
https://github.com/xslidi/EfficientNets_ddl_apex
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.