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 61–70 of 308 papers

TitleStatusHype
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
UniMERNet: A Universal Network for Real-World Mathematical Expression RecognitionCode3
XoFTR: Cross-modal Feature Matching TransformerCode2
Evolving Loss Functions for Specific Image Augmentation Techniques—0
Genetic Learning for Designing Sim-to-Real Data AugmentationsCode0
DiffClass: Diffusion-Based Class Incremental Learning—0
Outline-Guided Object Inpainting with Diffusion Models—0
Fiducial Focus Augmentation for Facial Landmark Detection—0
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion RecognitionCode0
Neural Loss Function Evolution for Large-Scale Image Classifier Convolutional Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified