https://github.com/malidib/acid
공식 코드A machine-generated catalogue of Charon's craters and implications for the Kuiper belt
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/marioduran/tyolov5
공식 코드https://github.com/mlgroup-jlu/llm-data-aug-survey
공식 코드https://github.com/moabarar/inaugment
공식 코드https://github.com/moreno98/vision-language-bias-control
공식 코드Improving Fairness using Vision-Language Driven Image Augmentation
https://github.com/mowangphy/HOPE-CNN
공식 코드Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and Estimation
https://github.com/multitel-ai/cia
공식 코드CIA: Controllable Image Augmentation Framework Based on Stable Diffusion
https://github.com/nust-machine-intelligence-laboratory/mccl
공식 코드Semi-supervised Semantic Segmentation with Multi-Constraint Consistency Learning
https://github.com/ocatak/malware_api_class
공식 코드Data Augmentation Based Malware Detection using Convolutional Neural Networks
https://github.com/ogrenenmakine/Policy-Gradient-Driven-Noise-Mask
공식 코드https://github.com/ondert/xoftr
공식 코드https://github.com/opendatalab/unimernet
공식 코드UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition
https://github.com/phy710/icassp2024-fdg-st
공식 코드Domain Generalization with Fourier Transform and Soft Thresholding
https://github.com/physiologicailab/sam-cl
공식 코드Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial Images
https://github.com/qinhongda8/ldds
공식 코드Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection
https://github.com/roatienza/straug
공식 코드https://github.com/ruizhe-l/tgb-gan
공식 코드https://github.com/ruizvitor/ANDA
공식 코드ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection
https://github.com/ryanhangzhou/sac-gan
공식 코드https://github.com/scuwyh2000/diff-ii
공식 코드Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce Classification
https://github.com/sugarfreemanatee/feature-space-augmentation-and-iterated-learning
공식 코드Long Tail Image Generation Through Feature Space Augmentation and Iterated Learning
https://github.com/swager/levythin
공식 코드https://github.com/t-systems-on-site-services-gmbh/coocop
공식 코드Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing
https://github.com/tensorflow/models
공식 코드https://github.com/tensorflow/tpu
공식 코드https://github.com/tensorflow/tpu/tree/master/models/official/detection/projects/copy_paste
공식 코드Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
https://github.com/tobiastrein/hsc-reident
공식 코드Siamese Networks for Cat Re-Identification: Exploring Neural Models for Cat Instance Recognition
https://github.com/tonmoy-hossain/mgaug
공식 코드MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image Deformations
https://github.com/ty-97/seletive-volume-mix
공식 코드https://github.com/uncbiag/easyreg
공식 코드Anatomical Data Augmentation via Fluid-based Image Registration
https://github.com/uoguelph-mlrg/Cutout
공식 코드Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/uqzhichen/haszsl
공식 코드https://github.com/victorychain/Adversarial-Policy-Gradient-Augmentation
공식 코드Adversarial Policy Gradient for Deep Learning Image Augmentation
https://github.com/visinf/diagen
공식 코드https://github.com/vturrisi/disef
공식 코드Diversified in-domain synthesis with efficient fine-tuning for few-shot classification
https://github.com/wenliangdai/vlp-object-hallucination
공식 코드Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.