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 51–75 of 308 papers

TitleStatusHype
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from PixelsCode1
An Open-source Tool for Hyperspectral Image Augmentation in TensorflowCode1
Can AI help in screening Viral and COVID-19 pneumonia?Code1
Learn to Augment: Joint Data Augmentation and Network Optimization for Text RecognitionCode1
What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network PerformanceCode1
Kornia: an Open Source Differentiable Computer Vision Library for PyTorchCode1
Implicit Semantic Data Augmentation for Deep NetworksCode1
Adversarial Policy Gradient for Deep Learning Image AugmentationCode1
Learning Data Augmentation Strategies for Object DetectionCode1
Fast AutoAugmentCode1
Unsupervised Data Augmentation for Consistency TrainingCode1
Improved Regularization of Convolutional Neural Networks with CutoutCode1
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
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
Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled DataCode0
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching—0
Diffusion Models for Robotic Manipulation: A Survey—0
An Empirical Study of Validating Synthetic Data for Text-Based Person RetrievalCode0
Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks—0
Show:102550
← PrevPage 3 of 13Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified