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 151–160 of 308 papers

TitleStatusHype
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
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
Progressive Random Convolutions for Single Domain Generalization—0
Bias mitigation techniques in image classification: fair machine learning in human heritage collections—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified