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

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Showing 11–20 of 308 papers

TitleStatusHype
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documentsCode2
Random Erasing Data AugmentationCode2
Prompt-Free Conditional Diffusion for Multi-object Image AugmentationCode1
Image, Text, and Speech Data Augmentation using Multimodal LLMs for Deep Learning: A SurveyCode1
Swin Transformer with Enhanced Dropout and Layer-wise Unfreezing for Facial Expression Recognition in Mental Health DetectionCode1
Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce ClassificationCode1
DIAGen: Diverse Image Augmentation with Generative ModelsCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
CLAP: Isolating Content from Style through Contrastive Learning with Augmented PromptsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified