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 1–10 of 308 papers

TitleStatusHype
Prompt-Free Conditional Diffusion for Multi-object Image AugmentationCode1
rQdia: Regularizing Q-Value Distributions With Image Augmentation—0
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models—0
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention—0
Camera-based method for the detection of lifted truck axles using convolutional neural networks—0
When Large Multimodal Models Confront Evolving Knowledge:Challenges and PathwaysCode2
Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys—0
Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder—0
Language-Driven Dual Style Mixing for Single-Domain Generalized Object DetectionCode0
Batch Augmentation with Unimodal Fine-tuning for Multimodal LearningCode0
Show:102550
← PrevPage 1 of 31Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified