https://github.com/chunmeifeng/difftpt
공식 코드Diffusion-Enhanced Test-time Adaptation with Text and Image Augmentation
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/cis-ncbj/covid19-stylegan-augmentation
공식 코드Performance of GAN-based augmentation for deep learning COVID-19 image classification
https://github.com/cjrd/self-supervised-pretraining
공식 코드Self-Supervised Pretraining Improves Self-Supervised Pretraining
https://github.com/convergedmachine/Policy-Gradient-Driven-Noise-Mask
공식 코드https://github.com/convergedmachine/refined-radimagenet
공식 코드https://github.com/darkpromise98/iaa
공식 코드Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning
https://github.com/dengpingfan/sodbenchmark
공식 코드https://github.com/denisyarats/drq
공식 코드Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
https://github.com/derekabc/gans-agriculture
공식 코드Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review
https://github.com/dipuk0506/multimodal
공식 코드Batch Augmentation with Unimodal Fine-tuning for Multimodal Learning
https://github.com/dreamflake/odi
공식 코드Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
https://github.com/drlxj/camdiff
공식 코드https://github.com/edm-research/genetic-augment
공식 코드Genetic Learning for Designing Sim-to-Real Data Augmentations
https://github.com/faysal-md/an-interpretable-deep-learning-approach-for-skin-cancer-categorization-ieee2023
공식 코드An Interpretable Deep Learning Approach for Skin Cancer Categorization
https://github.com/fistyee/mixpro
공식 코드MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer
https://github.com/flame-chasers/syntbpr
공식 코드An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval
https://github.com/frankkramer-lab/ensmic
공식 코드https://github.com/frankkramer-lab/riadd.aucmedi
공식 코드Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models
https://github.com/google-research/fitvid
공식 코드https://github.com/google-research/uda
공식 코드https://github.com/hahnec/polar_augment
공식 코드Isometric Transformations for Image Augmentation in Mueller Matrix Polarimetry
https://github.com/haohang96/mra
공식 코드https://github.com/humza909/survery-image-mixing-and-deleting-for-data-augmentation
공식 코드https://github.com/iyatomilab/rsgaia_hbbt
공식 코드https://github.com/jackryo/ricap
공식 코드Data Augmentation using Random Image Cropping and Patching for Deep CNNs
https://github.com/jaychempan/ETS
공식 코드https://github.com/jmdvinodjmd/hcr-net
공식 코드HCR-Net: A deep learning based script independent handwritten character recognition network
https://github.com/jpzhang1810/PAM
공식 코드Improving the Transferability of Adversarial Samples by Path-Augmented Method
https://github.com/kakaobrain/fast-autoaugment
공식 코드https://github.com/kornia/kornia
공식 코드Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
https://github.com/laboratoryopticsbiosciences/bioimageloader
공식 코드BioImageLoader: Easy Handling of Bioimage Datasets for Machine Learning
https://github.com/lisadunlap/alia
공식 코드Diversify Your Vision Datasets with Automatic Diffusion-Based Augmentation
https://github.com/lostxine/elo-rainbow
공식 코드Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?
https://github.com/mabdelhack/hyperspectral_image_generator
공식 코드An Open-source Tool for Hyperspectral Image Augmentation in Tensorflow
https://github.com/mahdi65/roadDamageDetection2020
공식 코드An Efficient and Scalable Deep Learning Approach for Road Damage Detection
https://github.com/mahmoudnafifi/WB_color_augmenter
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.