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 101–125 of 308 papers

TitleStatusHype
MiAMix: Enhancing Image Classification through a Multi-stage Augmented Mixed Sample Data Augmentation Method—0
Attention-Driven Lightweight Model for Pigmented Skin Lesion Detection—0
Zero-Shot Learning by Harnessing Adversarial SamplesCode0
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review—0
Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding—0
RaViTT: Random Vision Transformer Tokens—0
Exploring Simple, High Quality Out-of-Distribution Detection with L2 Normalization—0
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision TransformerCode1
INK: Inheritable Natural Backdoor Attack Against Model Distillation—0
Realistic Data Enrichment for Robust Image Segmentation in Histopathology—0
Performance of GAN-based augmentation for deep learning COVID-19 image classificationCode0
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
Progressive Random Convolutions for Single Domain Generalization—0
Improving the Transferability of Adversarial Samples by Path-Augmented MethodCode1
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image SegmentationCode0
Towards Composable Distributions of Latent Space Augmentations—0
BioImageLoader: Easy Handling of Bioimage Datasets for Machine LearningCode0
LMSeg: Language-guided Multi-dataset Segmentation—0
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification—0
A convolutional neural network of low complexity for tumor anomaly detection—0
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay—0
Development of a Prototype Application for Rice Disease Detection Using Convolutional Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified