데이터 증강 완벽 가이드! 과적합 방지와 모델 성능 향상의 핵심 기법. 이미지 증강: Mixup vs CutMix vs AugMix 비교, AutoAugment vs RandAugment 성능/비용. NLP 증강: Back Translation, EDA 4가지 연산, LLM 활용 최신 기법. GAN 기반 증강으로 민감도 10% 향상!
#AugMix #AutoAugment #BackTranslation #CutMix #Cutout #DataAugmentation #EDA
doyouknow.kr/631/data-aug...
#AugMix #AutoAugment #BackTranslation #CutMix #Cutout #DataAugmentation #EDA
doyouknow.kr/631/data-aug...
데이터 증강 완벽 가이드: AI 학습 데이터가 부족할 때의 마법! Mixup, CutMix, AutoAugment 총정리
데이터 증강 완벽 가이드! 과적합 방지와 모델 성능 향상의 핵심 기법. 이미지 증강: Mixup vs CutMix vs AugMix 비교, AutoAugment vs RandAugment 성능/비용. NLP 증강: Back Translation, EDA 4가지 연산, LLM 활용 최신 기법. GAN 기반 증강으로 민감도 10% 향상!
doyouknow.kr
December 5, 2025 at 12:26 PM
데이터 증강 완벽 가이드! 과적합 방지와 모델 성능 향상의 핵심 기법. 이미지 증강: Mixup vs CutMix vs AugMix 비교, AutoAugment vs RandAugment 성능/비용. NLP 증강: Back Translation, EDA 4가지 연산, LLM 활용 최신 기법. GAN 기반 증강으로 민감도 10% 향상!
#AugMix #AutoAugment #BackTranslation #CutMix #Cutout #DataAugmentation #EDA
doyouknow.kr/631/data-aug...
#AugMix #AutoAugment #BackTranslation #CutMix #Cutout #DataAugmentation #EDA
doyouknow.kr/631/data-aug...
disease fundus datasets. Our contributions are threefold. (1) We introduce MediAug, a comprehensive and reproducible benchmark for advanced data augmentation in medical imaging. (2) We systematically evaluate MixUp, YOCO, CropMix, CutMix, AugMix, and [4/7 of https://arxiv.org/abs/2504.18983v1]
April 29, 2025 at 5:59 AM
disease fundus datasets. Our contributions are threefold. (1) We introduce MediAug, a comprehensive and reproducible benchmark for advanced data augmentation in medical imaging. (2) We systematically evaluate MixUp, YOCO, CropMix, CutMix, AugMix, and [4/7 of https://arxiv.org/abs/2504.18983v1]
Haoyang Li, Wei Chen, Xiaojin Zhang
Fed-AugMix: Balancing Privacy and Utility via Data Augmentation
https://arxiv.org/abs/2412.13818
Fed-AugMix: Balancing Privacy and Utility via Data Augmentation
https://arxiv.org/abs/2412.13818
December 19, 2024 at 5:53 AM
Haoyang Li, Wei Chen, Xiaojin Zhang
Fed-AugMix: Balancing Privacy and Utility via Data Augmentation
https://arxiv.org/abs/2412.13818
Fed-AugMix: Balancing Privacy and Utility via Data Augmentation
https://arxiv.org/abs/2412.13818