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 231–240 of 308 papers

TitleStatusHype
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified