Image Augmentation
98 papers with code • 1 benchmarks • 1 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Image Augmentation
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find Image Augmentation models and implementations
Intel Image Classification
Latest papers
UniMERNet: A Universal Network for Real-World Mathematical Expression Recognition
This paper presents the UniMER dataset to provide the first study on Mathematical Expression Recognition (MER) towards complex real-world scenarios.
A Survey on Data Augmentation in Large Model Era
mlgroup-jlu/llm-data-aug-survey
•
•
Leveraging large models, these data augmentation techniques have outperformed traditional approaches.
MGAug: Multimodal Geometric Augmentation in Latent Spaces of Image Deformations
In this paper, we propose a novel model, Multimodal Geometric Augmentation (MGAug), that for the first time generates augmenting transformations in a multimodal latent space of geometric deformations.
An Interpretable Deep Learning Approach for Skin Cancer Categorization
Our models decision-making process can be clarified because of the implementation of explainable artificial intelligence (XAI).
Diversified in-domain synthesis with efficient fine-tuning for few-shot classification
vturrisi/disef
•
•
Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class.
Improving Fairness using Vision-Language Driven Image Augmentation
moreno98/vision-language-bias-control
•
•
These paths are then applied to augment images to improve the fairness of a given dataset.
Domain Generalization with Fourier Transform and Soft Thresholding
However, it neglects background interference in the amplitude spectrum.
MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalization
Extensive experiments validate the effectiveness of MLN-net in segmenting clustered microcalcifications from different domains and the its segmentation accuracy surpasses state-of-the-art methods.
Zero-Shot Learning by Harnessing Adversarial Samples
uqzhichen/haszsl
•
•
To take the advantage of image augmentations while mitigating the semantic distortion issue, we propose a novel ZSL approach by Harnessing Adversarial Samples (HAS).
Diversify Your Vision Datasets with Automatic Diffusion-Based Augmentation
lisadunlap/alia
•
•
NeurIPS 2023
As such, we explore how natural language descriptions of the domains seen in training data can be used with large vision models trained on diverse pretraining datasets to generate useful variations of the training data.
