Image Augmentation
101 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
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Libraries
Use these libraries to find Image Augmentation models and implementations
Intel Image Classification
Latest papers
Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial Images
Limited availability of datasets from unconstrained settings further limits the use of the state-of-the-art segmentation networks, loss functions and learning strategies which have been built and validated for RGB images.
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised Learning
In order to extract and separate compound figures into usable individual images for downstream learning, we propose a simple compound figure separation (SimCFS) framework without using the traditionally required detection bounding box annotations, with a new loss function and a hard case simulation.
Image augmentation improves few-shot classification performance in plant disease recognition
With the world population projected to near 10 billion by 2050, minimizing crop damage and guaranteeing food security has never been more important.
A machine-generated catalogue of Charon's craters and implications for the Kuiper belt
malidib/acid
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This is motivated by the recent results of Singer et al. (2019) who, using manual cataloging, found a change in the size distribution slope of craters smaller than 12 km in diameter, translating into a paucity of small Kuiper Belt objects.
Masked Autoencoders are Robust Data Augmentors
Specifically, MRA consistently enhances the performance on supervised, semi-supervised as well as few-shot classification.
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?
We investigate whether self-supervised learning (SSL) can improve online reinforcement learning (RL) from pixels.
Deep PCB To COCO Convertor
It has 1500 image pairs.
AugStatic - A Light-Weight Image Augmentation Library
avs-abhishek123/AugStatic
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Journal of Emerging Technologies and Innovative Research (JETIR) 2022
AugStatic is a custom-built image augmentation library with lower computation costs and more extraordinary salient features compared to other image augmentation libraries.
Augmented Balanced Image Dataset Generator Using AugStatic Library
avs-abhishek123/Balanced_Augmented_ImageDatasetGenerator
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International Journal Of Research And Analytical Reviews (IJRAR) 2022
This paper focuses on the image dataset generator that balances an imbalanced dataset using the AugStatic augmentation library.
Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review
In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e. g., image classification, segmentation, object detection and localization), in the presence of challenges with biological variability and unstructured environments.
