https://github.com/wsuagrobotics/data-aug-multi-modal-llm
공식 코드Image, Text, and Speech Data Augmentation using Multimodal LLMs for Deep Learning: A Survey
Papers with Code 아카이브
Image Augmentation
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Image Augmentation is a data augmentation method that generates more training data from the existing training samples. Image Augmentation is especially useful in domains where training data is limited or expensive to obtain like in biomedical applications.
Source: Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing
( Image credit: Kornia )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xingzhehe/ganseg
공식 코드GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation
https://github.com/yezanting/mln-net-verson1
공식 코드https://github.com/yuanjianhao508/notjustprettypictures
공식 코드Not Just Pretty Pictures: Toward Interventional Data Augmentation Using Text-to-Image Generators
https://github.com/zdk258/TokenSwap
공식 코드Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation
https://github.com/zhangxiaozao/BO-Aug
공식 코드Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification Tasks
https://github.com/zhunzhong07/Random-Erasing
공식 코드https://github.com/2023-MindSpore-1/ms-code-16/tree/main/autoaugment
https://github.com/2023-MindSpore-1/ms-code-211/tree/main/densenet
https://github.com/2023-MindSpore-1/ms-code-212/tree/main/efficientnet-b0
An Efficient and Scalable Deep Learning Approach for Road Damage Detection
https://github.com/2023-MindSpore-4/Code3/tree/main/densenet
https://github.com/2023-MindSpore-4/Code8/tree/main/autoaugment
https://github.com/2024-MindSpore-1/Code6/tree/main/autoaugment
https://github.com/2han9x1a0release/RLCC
https://github.com/4uiiurz1/pytorch-auto-augment
https://github.com/A-Telfer/AugKey
https://github.com/Brunogomes97/Imdb
https://github.com/Canjie-Luo/Scene-Text-Image-Transformer
Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition
https://github.com/ChenXiao61/Img_augmentor
Augmentor: An Image Augmentation Library for Machine Learning
https://github.com/Ching-Chen-Wang/EfficientNet-eLite
https://github.com/ChingHuanChiu/sensitive
https://github.com/CoinCheung/SphereReID
https://github.com/DeepVoltaire/AutoAugment
https://github.com/IyatomiLab/CE-CLCNN
https://github.com/Janghyeonwoong/Random-Erasing-Tensorflow2
https://github.com/Jasonlee1995/AutoAugment_Detection
https://github.com/Jiaming-Liu/BayerUnifyAug
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
https://github.com/KentaItakura/CNN-classification-using-random-erasing-and-cut-out
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/LMaxence/Cifar10_Classification
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/MindSpore-paper-code-3/code1/tree/main/autoaugment
https://github.com/MindSpore-paper-code-3/code6/tree/main/autoaugment
https://github.com/Mjrovai/covid19Xray
https://github.com/NVlabs/DG-Net
https://github.com/NoamRosenberg/fast-autoaugment-on-a-single-gpu
https://github.com/PaddlePaddle/PaddleClas
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/PaddlePaddle/PaddleOCR
Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.