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/Burf/EfficientNet-Lite-Tensorflow2
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/CSautier/Breakout
https://github.com/CSautier/PongBot
https://github.com/Cadene/pretrained-models.pytorch
https://github.com/CanyonWind/Single-Path-One-Shot-NAS-MXNet
Single Path One-Shot Neural Architecture Search with Uniform Sampling
https://github.com/ChintanThacker/Facial_Expression_EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Crevass/Hybrid-Agent
https://github.com/Cyprien0105/DataScience
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/D-X-Y/GDAS
https://github.com/D-X-Y/NATS-Bench
NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size
https://github.com/DDGRCF/yolov5_Chinese
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/DDGRCF/yolov5_pruning
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/DIG-Beihang/RobustART
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques
https://github.com/DLR-RM/stable-baselines3
https://github.com/DMIU-ShELL/MOSAIC
https://github.com/DableUTeeF/keras-efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/DataCanvasIO/Hypernets
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
https://github.com/DataXujing/PNASNet_pytorch
https://github.com/DaveKim3872/nasbench-hpc
NAS-Bench-101: Towards Reproducible Neural Architecture Search
https://github.com/DavidCastilloAlvarado/Path-planning-and-Reinforcement-Learning
https://github.com/DavidLandup0/deepvision
https://github.com/Dawars/auto_deeplab-pytorch
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/Deci-AI/super-gradients
https://github.com/DeepBrainsMe/FSnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/DevSlem/AINE-DRL
https://github.com/DoctorLiQ/AutoML
https://github.com/DuanYiqun/Auto-ReID-Fast
Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification
https://github.com/EMI-Group/TransferLearning-Tasks
RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning
https://github.com/EconomistGrant/HTFE-tensortrade
https://github.com/EstherBear/implementation-of-network-slimming
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/EvgeniyBochenkov/github-move
https://github.com/Ezereal/enas
https://github.com/FMArduini/python-rl
https://github.com/FaceOnLive/Face-Liveness-Detection-SDK-Android
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
https://github.com/FaceOnLive/Face-Recognition-SDK-Android
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.