https://github.com/2023-MindSpore-4/Code3/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/2023-MindSpore-4/Code7/tree/main/single_path_nas
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/2023-MindSpore-4/Code7/tree/main/sr_ea
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
https://github.com/2023-MindSpore-4/Code8/tree/main/Auto-DeepLab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/2023-MindSpore-4/Code8/tree/main/AutoSlim
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
https://github.com/2023-MindSpore-4/Code9/tree/main/RetinaNet
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/2024-MindSpore-1/Code4/tree/main/MobileNet/mobilenetv3
https://github.com/2024-MindSpore-1/Code5/tree/main/ArbitraryStyleTransfer
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/2024-MindSpore-1/Code6/tree/main/ArbitraryStyleTransfer
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/2024-MindSpore-1/Code6/tree/main/Auto-DeepLab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/2024-MindSpore-1/Code6/tree/main/AutoSlim
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
https://github.com/2mawi2/master-thesis-experiments
https://github.com/6210612757/facerecognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ASzot/ppo-pytorch
https://github.com/ActiveVisionLab/NUQ
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes
https://github.com/Adlik/model_optimizer
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
https://github.com/AhmadQasim/proxylessnas-dense
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/AlvinIsonomia/darts.pytorch1.3_MultiGPU
https://github.com/AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Andrew-Qibin/CoordAttention
Rethinking Bottleneck Structure for Efficient Mobile Network Design
https://github.com/AngusDujw/FlatTrajectoryDistillation_FTD
Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation
https://github.com/AnjieCheng/InstaNAS
https://github.com/AnjieZheng/InstaNAS
https://github.com/Aravind-11/AI-Gaming
https://github.com/Aravind-11/IITM_Saastra
https://github.com/Aravind-11/Multi-Agent-RL
https://github.com/Atze00/MoViNet-pytorch
MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/AutoML-4Paradigm/ERAS
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding
https://github.com/AutoML-4Paradigm/S2S
Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding
https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/PPOAgent.py
https://github.com/BayesWatch/cinic-10
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets
https://github.com/BenjiKCF/EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/BerkeleyLearnVerify/VerifAI
https://github.com/BingLiHanShuang/AutoGluon_IntegrateSimpleFeedforward
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
https://github.com/BobMcDear/pytorch-efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.