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 101–110 of 308 papers

TitleStatusHype
MiAMix: Enhancing Image Classification through a Multi-stage Augmented Mixed Sample Data Augmentation Method—0
Attention-Driven Lightweight Model for Pigmented Skin Lesion Detection—0
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
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision TransformerCode1
INK: Inheritable Natural Backdoor Attack Against Model Distillation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AugstaticBalanced Accuracy0—Unverified