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 151–175 of 308 papers

TitleStatusHype
Development of a Prototype Application for Rice Disease Detection Using Convolutional Neural Networks—0
Diagnosis of COVID-19 based on Chest Radiography—0
DiffClass: Diffusion-Based Class Incremental Learning—0
Diffusion Models for Robotic Manipulation: A Survey—0
Document Layout Analysis with Aesthetic-Guided Image Augmentation—0
DT/MARS-CycleGAN: Improved Object Detection for MARS Phenotyping Robot—0
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model—0
DynASyn: Multi-Subject Personalization Enabling Dynamic Action Synthesis—0
Efficient Augmentation via Data Subsampling—0
Improving Deep Learning-based Defect Detection on Window Frames with Image Processing Strategies—0
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching—0
Inter-slice image augmentation based on frame interpolation for boosting medical image segmentation accuracy—0
Landslide Geohazard Assessment With Convolutional Neural Networks Using Sentinel-2 Imagery Data—0
Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization—0
INK: Inheritable Natural Backdoor Attack Against Model Distillation—0
Learning More with Less: GAN-based Medical Image Augmentation—0
Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge—0
Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints—0
Leveraging Habitat Information for Fine-grained Bird Identification—0
LMSeg: Language-guided Multi-dataset Segmentation—0
Medical Image Generation using Generative Adversarial Networks—0
MiAMix: Enhancing Image Classification through a Multi-stage Augmented Mixed Sample Data Augmentation Method—0
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pre-training—0
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pretraining—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified