https://github.com/MindSpore-paper-code-3/code8/tree/main/efficientnet-b0
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/MindSpore-scientific/code-12/tree/main/GraphPAS
GraphPAS: Parallel Architecture Search for Graph Neural Networks
https://github.com/MindSpore-scientific/code-5/tree/main/SeesawLoss-master
Seesaw-Net: Convolution Neural Network With Uneven Group Convolution
https://github.com/MrD1360/Keras-implementation-of-MobileNetv3-with-L-RASSP
https://github.com/MrSyee/pg-is-all-you-need
https://github.com/NACLab/robust-active-inference
https://github.com/NVIDIA/DeepLearningExamples
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Narsil/rl-baselines
https://github.com/NatGr/Master_Thesis
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
https://github.com/Nathnos/Article-Reproduction-Deep-Learning-Efficient-Joint-Neural-Architecture-and-Hyperparameter-Search
Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search
https://github.com/NervanaSystems/coach
https://github.com/NervanaSystems/distiller
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/NivNayman/XNAS
https://github.com/NoamRosenberg/autodeeplab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/Nordeus/heroic-rl
https://github.com/OFANAS/OFANAS_PerformanceEstimation
Generalized Latency Performance Estimation for Once-For-All Neural Architecture Search
https://github.com/OctThe16th/PPO-Keras
https://github.com/OctopusMind/RLHF_PPO
https://github.com/OniroAI/Instance-segmentation-with-MobileNetV3
https://github.com/Ostyk/walk-bot
https://github.com/PaddlePaddle/PaddleClas
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleOCR
https://github.com/PatrickZH/Improved-Deep-Leakage-from-Gradients
https://github.com/Pointcept/Pointcept
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/Pol22/NAS_DIP
https://github.com/PolinaDruzhinina/mobilenet
https://github.com/PolinaDruzhinina/modelnet
https://github.com/PotatoSpudowski/CactiNet
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/Prev/downsampled-imagenet-path-fixer
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
https://github.com/RAIVNLab/STR
https://github.com/Rafael1s/Deep-Reinforcement-Learning-Udacity
https://github.com/RamposhPidrov/waifu2face
https://github.com/Randl/MobileNetV3-pytorch
https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
https://github.com/RhythmSyed/NAS_PerformanceEstimation
Generalized Latency Performance Estimation for Once-For-All Neural Architecture Search
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.