https://github.com/huynhtuan17ti/AI4VN-Hackathon2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
AutoReCon: Neural Architecture Search-based Reconstruction for Data-free Compression
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Learning from Mistakes -- A Framework for Neural Architecture Search
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.