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 151–200 of 308 papers

TitleStatusHype
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
Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts—0
Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images—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
Multi-Classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network—0
Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology—0
Multi-visual modality micro drone-based structural damage detection—0
Neural Loss Function Evolution for Large-Scale Image Classifier Convolutional Neural Networks—0
Neural Networks for Semantic Gaze Analysis in XR Settings—0
OASIS: Offsetting Active Reconstruction Attacks in Federated Learning—0
Object-Based Augmentation Improves Quality of Remote Sensing Semantic Segmentation—0
On the Impact of Interpretability Methods in Active Image Augmentation Method—0
Outline-Guided Object Inpainting with Diffusion Models—0
Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation—0
Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-19—0
Pneumonia Detection in Chest X-Rays using Neural Networks—0
Polarimetric image augmentation—0
Pose-Aware Instance Segmentation Framework from Cone Beam CT Images for Tooth Segmentation—0
Progressive Random Convolutions for Single Domain Generalization—0
Randomize to Generalize: Domain Randomization for Runway FOD Detection—0
RaViTT: Random Vision Transformer Tokens—0
Rawgment: Noise-Accounted RAW Augmentation Enables Recognition in a Wide Variety of Environments—0
Realistic Data Enrichment for Robust Image Segmentation in Histopathology—0
Reducing Labelled Data Requirement for Pneumonia Segmentation using Image Augmentations—0
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset—0
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies—0
rQdia: Regularizing Q-Value Distributions With Image Augmentation—0
SDNIA-YOLO: A Robust Object Detection Model for Extreme Weather Conditions—0
Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels—0
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay—0
Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications—0
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification—0
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified