Image Augmentation
95 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 )
Libraries
Use these libraries to find Image Augmentation models and implementationsLatest papers
A Survey on Data Augmentation in Large Model Era
Leveraging large models, these data augmentation techniques have outperformed traditional approaches.
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
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
These paths are then applied to augment images to improve the fairness of a given dataset.
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
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
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.
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer
TransMix uses unreliable attention maps to compute mixed attention labels that can affect the model.
Performance of GAN-based augmentation for deep learning COVID-19 image classification
Data augmentation is a typical methodology used in machine learning when confronted with a limited data set.
CamDiff: Camouflage Image Augmentation via Diffusion Model
Specifically, we leverage the latent diffusion model to synthesize salient objects in camouflaged scenes, while using the zero-shot image classification ability of the Contrastive Language-Image Pre-training (CLIP) model to prevent synthesis failures and ensure the synthesized object aligns with the input prompt.