https://github.com/00why00/pfcd
공식 코드Prompt-Free Conditional Diffusion for Multi-object 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/BreastGAN/augmentation
공식 코드Adversarial Augmentation for Enhancing Classification of Mammography Images
https://github.com/Canjie-Luo/Text-Image-Augmentation
공식 코드Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition
https://github.com/DebeshJha/Kvasir-Instrument
공식 코드https://github.com/DengPingFan/SOC-DataAug
공식 코드https://github.com/DevashishPrasad/CascadeTabNet
공식 코드https://github.com/EVOKE-LMM/EVOKE
공식 코드When Large Multimodal Models Confront Evolving Knowledge:Challenges and Pathways
https://github.com/Evizero/Augmentor.jl
공식 코드Augmentor: An Image Augmentation Library for Machine Learning
https://github.com/Gahyeonkim09/AAPL
공식 코드AAPL: Adding Attributes to Prompt Learning for Vision-Language Models
https://github.com/HazyResearch/tanda
공식 코드Learning to Compose Domain-Specific Transformations for Data Augmentation
https://github.com/JBall1/Few-Shot-Limited-Data
공식 코드https://github.com/LinxiFan/SECANT
공식 코드SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies
https://github.com/LostXine/elo-sac
공식 코드Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?
https://github.com/MegviiDetection/FSCE
공식 코드FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
https://github.com/NesterukSergey/segmentation_image_augmentation
공식 코드Image Augmentation for Multitask Few-Shot Learning: Agricultural Domain Use-Case
https://github.com/Neuralwood-Net/woodnet
공식 코드Application of Facial Recognition using Convolutional Neural Networks for Entry Access Control
https://github.com/PrasannaPulakurthi/Foreground-Background-Augmentation
공식 코드Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled Data
https://github.com/SENSE-Lab-OSU/mstar_data_aug
공식 코드Sparse Signal Models for Data Augmentation in Deep Learning ATR
https://github.com/TitorX/CFR-ICL-Interactive-Segmentation
공식 코드CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation
https://github.com/Walleclipse/iWildCam_2019_FGVC6
공식 코드https://github.com/YichaoCai1/CLAP
공식 코드CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts
https://github.com/ZohebAbai/Tiny-ImageNet-Challenge
공식 코드https://github.com/akitotakeki/pgp-chainer
공식 코드https://github.com/albu/albumentations
공식 코드https://github.com/alperctnkaya/fedaug
공식 코드https://github.com/anguelos/tormentor
공식 코드TorMentor: Deterministic dynamic-path, data augmentations with fractals
https://github.com/anisamziane/rawtexture
공식 코드Learning deep illumination-robust features from multispectral filter array images
https://github.com/arcelien/pba
공식 코드Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
https://github.com/arraiyopensource/kornia
공식 코드https://github.com/automl/hard-view-selection
공식 코드https://github.com/automl/pretraining-hard-views
공식 코드https://github.com/blackfeather-wang/ISDA-for-Deep-Networks
공식 코드https://github.com/bubblybingbong/augmentationsegmentation
공식 코드https://github.com/ceciliaresearch/MixedExample
공식 코드https://github.com/chenmingxiang110/AugNet
공식 코드AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation
https://github.com/chenz418/viis
공식 코드VIIS: Visible and Infrared Information Synthesis for Severe Low-light Image Enhancement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.