https://github.com/Cyprien0105/DataScience
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Papers with Code 아카이브
AutoML
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 4
- 보존된 평가 기록
- 11
Automated Machine Learning (AutoML) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection. Source: Evaluating recommender systems for AI-driven data science (1905.09205)
Source: CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/D3-AI/Cardea
Cardea: An Open Automated Machine Learning Framework for Electronic Health Records
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/DataSeeds-ai/DSD-finetune-blip-llava
https://github.com/DataXujing/EfficientDet_pytorch
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/Duan-JM/awesome-papers-fewshot
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
https://github.com/Fedzbar/laser-release
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
https://github.com/GAIA-vision/GAIA-cv
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models
https://github.com/GiadaSimionato/Reasoning_Agents_2020
https://github.com/GiuliaLanzillotta/INAS
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.