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
Towards Data-Centric Face Anti-Spoofing: Improving Cross-domain Generalization via Physics-based Data SynthesisCode0
LowCLIP: Adapting the CLIP Model Architecture for Low-Resource Languages in Multimodal Image Retrieval TaskCode0
Enhancing Autonomous Vehicle Perception in Adverse Weather through Image Augmentation during Semantic Segmentation TrainingCode0
Learning deep illumination-robust features from multispectral filter array imagesCode0
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model—0
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions—0
Evaluation and Comparison of Emotionally Evocative Image Augmentation Methods—0
SDNIA-YOLO: A Robust Object Detection Model for Extreme Weather Conditions—0
Semmeldetector: Application of Machine Learning in Commercial Bakeries—0
How to Augment for Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models?—0
How Quality Affects Deep Neural Networks in Fine-Grained Image Classification—0
Bayesian and Convolutional Networks for Hierarchical Morphological Classification of Galaxies—0
Long Tail Image Generation Through Feature Space Augmentation and Iterated LearningCode0
Policy Gradient-Driven Noise MaskCode0
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
Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN—0
Leveraging Habitat Information for Fine-grained Bird Identification—0
MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image DeformationsCode0
Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pretraining—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified