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/PhamNguyen97/TSA_pytorch
https://github.com/PhamQuocHuy1101/UDA-Image-classification
https://github.com/PsorTheDoctor/microarray-data
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/QQBrowserVideoSearch/CBVS-UniCLIP
https://github.com/RAF96/ifmo-2019-deep-learning-coursework
https://github.com/Rahul-Venugopal/Image-augmentation_1
Augmentor: An Image Augmentation Library for Machine Learning
https://github.com/RocketFlash/CAP_augmentation
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
https://github.com/SanghunYun/UDA_pytorch
https://github.com/SaraAmd/Semi-Supervised-Learning
https://github.com/TillBeemelmanns/auto-augment-tf2-operations
https://github.com/Trustworthy-AI-Group/TransferAttack
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
https://github.com/UnofficialJuliaMirror/Augmentor.jl-0612f1b9-51e2-5127-9fc2-313c368ba66d
Augmentor: An Image Augmentation Library for Machine Learning
https://github.com/UnofficialJuliaMirrorSnapshots/Augmentor.jl-0612f1b9-51e2-5127-9fc2-313c368ba66d
Augmentor: An Image Augmentation Library for Machine Learning
https://github.com/Ximilar-com/tf-image
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/YaCpotato/B4ResearchDeepaugment
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/YaCpotato/deepaugmentFix
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/YaoMarkMu/DRQTRANS
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
https://github.com/Zhiwei-Z/pba_experiment
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
https://github.com/abcp4/DAPytorch
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/adam-mehdi/MuarAugment
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
https://github.com/aditya30394/Person-Re-Identification
https://github.com/albumentations-team/albumentations
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/arraiyopensource/torchgeometry
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
https://github.com/ashokkumar-kola/FYP
Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models
https://github.com/avs-abhishek123/AugStatic
https://github.com/barisozmen/deepaugment
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/bhacquin/UDA_pytorch
https://github.com/cfld/amdim
https://github.com/changewOw/Cutout-numpy
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/chenyouxin113/sota-status-investigation
https://github.com/conradry/copy-paste-aug
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
https://github.com/cy0616/data_augment
https://github.com/dishen12/Cuout_modify
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/eaguaida/TF_EnsNet-
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/etetteh/sota-data-augmentation-and-optimizers
Improved Regularization of Convolutional Neural Networks with Cutout
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.