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 implementations
2 papers
37,721
2 papers
15,282
2 papers
9,242
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A Survey on Data Augmentation in Large Model Era

mlgroup-jlu/llm-data-aug-survey 27 Jan 2024

Leveraging large models, these data augmentation techniques have outperformed traditional approaches.

64
27 Jan 2024

An Interpretable Deep Learning Approach for Skin Cancer Categorization

faysal-md/an-interpretable-deep-learning-approach-for-skin-cancer-categorization-ieee2023 17 Dec 2023

Our models decision-making process can be clarified because of the implementation of explainable artificial intelligence (XAI).

2
17 Dec 2023

Diversified in-domain synthesis with efficient fine-tuning for few-shot classification

vturrisi/disef 5 Dec 2023

Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class.

10
05 Dec 2023

Improving Fairness using Vision-Language Driven Image Augmentation

moreno98/vision-language-bias-control 2 Nov 2023

These paths are then applied to augment images to improve the fairness of a given dataset.

7
02 Nov 2023

MLN-net: A multi-source medical image segmentation method for clustered microcalcifications using multiple layer normalization

yezanting/mln-net-verson1 6 Sep 2023

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.

4
06 Sep 2023

Zero-Shot Learning by Harnessing Adversarial Samples

uqzhichen/haszsl 1 Aug 2023

To take the advantage of image augmentations while mitigating the semantic distortion issue, we propose a novel ZSL approach by Harnessing Adversarial Samples (HAS).

1
01 Aug 2023

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.

53
25 May 2023

MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer

fistyee/mixpro 24 Apr 2023

TransMix uses unreliable attention maps to compute mixed attention labels that can affect the model.

16
24 Apr 2023

Performance of GAN-based augmentation for deep learning COVID-19 image classification

cis-ncbj/covid19-stylegan-augmentation 18 Apr 2023

Data augmentation is a typical methodology used in machine learning when confronted with a limited data set.

0
18 Apr 2023

CamDiff: Camouflage Image Augmentation via Diffusion Model

drlxj/camdiff 11 Apr 2023

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.

26
11 Apr 2023