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 226–250 of 308 papers

TitleStatusHype
Sparse Signal Models for Data Augmentation in Deep Learning ATRCode0
Simple Copy-Paste is a Strong Data Augmentation Method for Instance SegmentationCode1
Towards Performance Improvement in Indian Sign Language Recognition—0
Application of Facial Recognition using Convolutional Neural Networks for Entry Access ControlCode0
Differentiable Data Augmentation with KorniaCode3
An Efficient and Scalable Deep Learning Approach for Road Damage DetectionCode1
FusiformNet: Extracting Discriminative Facial Features on Different Levels—0
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopyCode1
A Comparative Study on Efficiencies of Variants of Convolutional Neural Networks based on Image Classification TaskCode0
CIMON: Towards High-quality Hash Codes—0
Data Augmentation Based Malware Detection using Convolutional Neural NetworksCode1
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch—0
Face Mask Detection using Transfer Learning of InceptionV3—0
A novel action recognition system for smart monitoring of elderly people using Action Pattern Image and Series CNN with transfer learning—0
A Technical Report for VIPriors Image Classification Challenge—0
Anatomical Data Augmentation via Fluid-based Image RegistrationCode1
Visual Question Generation from Radiology ImagesCode1
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning—0
Image Augmentations for GAN Training—0
Polarimetric image augmentation—0
Medical Image Generation using Generative Adversarial Networks—0
Temperate Fish Detection and Classification: a Deep Learning based Approach—0
Synthetic Image Augmentation for Damage Region Segmentation using Conditional GAN with Structure Edge—0
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from PixelsCode1
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documentsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified