SOTAVerified

Image Augmentation

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 )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–50 of 308 papers

TitleStatusHype
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from PixelsCode1
Improved Regularization of Convolutional Neural Networks with CutoutCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric LearningCode1
GANSeg: Learning to Segment by Unsupervised Hierarchical Image GenerationCode1
Can AI help in screening Viral and COVID-19 pneumonia?Code1
FSCE: Few-Shot Object Detection via Contrastive Proposal EncodingCode1
Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic ReviewCode1
Fast AutoAugmentCode1
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
CLAP: Isolating Content from Style through Contrastive Learning with Augmented PromptsCode1
Anatomical Data Augmentation via Fluid-based Image RegistrationCode1
Data Augmentation for Scene Text RecognitionCode1
Data Augmentation Based Malware Detection using Convolutional Neural NetworksCode1
Image, Text, and Speech Data Augmentation using Multimodal LLMs for Deep Learning: A SurveyCode1
An Efficient and Scalable Deep Learning Approach for Road Damage DetectionCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
Improving the Transferability of Adversarial Samples by Path-Augmented MethodCode1
AugNet: End-to-End Unsupervised Visual Representation Learning with Image AugmentationCode1
DIAGen: Diverse Image Augmentation with Generative ModelsCode1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
Learning Data Augmentation Strategies for Object DetectionCode1
FitVid: Overfitting in Pixel-Level Video PredictionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified