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 191–200 of 308 papers

TitleStatusHype
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning—0
A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening—0
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks—0
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch—0
A Tale of Color Variants: Representation and Self-Supervised Learning in Fashion E-Commerce—0
A Technical Report for ICCV 2021 VIPriors Re-identification Challenge—0
A Technical Report for VIPriors Image Classification Challenge—0
Attention-Driven Lightweight Model for Pigmented Skin Lesion Detection—0
Attention W-Net: Improved Skip Connections for better Representations—0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified