https://github.com/kdha0727/lung-opacity-and-covid-chest-x-ray-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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
Butterfly Transform: An Efficient FFT Based Neural Architecture Design
LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search
Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference
ISyNet: Convolutional Neural Networks design for AI accelerator
ISyNet: Convolutional Neural Networks design for AI accelerator
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Distributional bias compromises leave-one-out cross-validation
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective
Exploring Randomly Wired Neural Networks for Image Recognition
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.