https://github.com/felixgwu/img_classification_pk_pytorch
Improved Regularization of Convolutional Neural Networks with Cutout
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/hemanth346/mde_bs
https://github.com/hmnth1/table_ocr
https://github.com/hun1204/DeepLearning_Visualization_Basic
https://github.com/ildoonet/pytorch-randaugment
https://github.com/ildoonet/unsupervised-data-augmentation
https://github.com/ilyak93/GAIN-pytorch
https://github.com/joannayu25/NLP_Project_MIDS-W266
https://github.com/junkwhinger/fastautoaugment_jsh
https://github.com/kakaobrain/autoclint
https://github.com/kekmodel/UDA-pytorch
https://github.com/khanrc/pt.fractalnet
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/kschwethelm/hyperboliccv
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/lantgabor/Unsupervised-Data-Augmentation-PyTorch
https://github.com/layumi/Person_reID_baseline_pytorch
https://github.com/layumi/University1652-Baseline
https://github.com/leblancdaniel/paraphraser
https://github.com/lewis-morris/image_augment
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/lnstadrum/fastaugment
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/lyxxn0414/test-data-generation
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/manyids2/kornia
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
https://github.com/megvii-research/fsce
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
https://github.com/microsoft/Mask-based-Latent-Reconstruction
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
https://github.com/mingsun-tse/good-da-in-kd
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/mrvoh/meta_learning_multilingual_doc_classification
https://github.com/mv-lab/kuzushiji-recognition
https://github.com/namakemono/kaggle-alaska2-image-steganalysis
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/northeastsquare/effficientnet
https://github.com/ofa-sys/chinese-clip
https://github.com/open-mmlab/mmdetection
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
https://github.com/open-mmlab/mmpose
https://github.com/open-mmlab/mmpretrain
https://github.com/oxygen0605/ImageClassification
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/peisuke/UnsupervisedDataAugmentation.pytorch
https://github.com/philip-bachman/amdim-public
https://github.com/pksvision/Deep-WaveNet-Underwater-Image-Restoration
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.