Image Augmentation
102 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
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.
TorMentor: Deterministic dynamic-path, data augmentations with fractals
We propose the use of fractals as a means of efficient data augmentation.
Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer Sensors
Ruhallah93/Driver-Identification
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IEEE Sensors Journal 2022
After extracting features from smartphone-embedded sensors, various machine learning methods can be used to identify the driver.
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
Trustworthy-AI-Group/TransferAttack
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CVPR 2022
To tackle this limitation, we propose the object-based diverse input (ODI) method that draws an adversarial image on a 3D object and induces the rendered image to be classified as the target class.
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks
However, it is still an open question to what extent as well as which ensemble learning strategies are beneficial in deep learning based medical image classification pipelines.
SAC-GAN: Structure-Aware Image Composition
Specifically, our network takes the semantic layout features from the input scene image, features encoded from the edges and silhouette in the input object patch, as well as a latent code as inputs, and generates a 2D spatial affine transform defining the translation and scaling of the object patch.
Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image Augmentation
Federated Learning (FL) is a suitable solution for making use of sensitive data belonging to patients, people, companies, or industries that are obligatory to work under rigid privacy constraints.
GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation
xingzhehe/ganseg
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CVPR 2022
Segmenting an image into its parts is a frequent preprocess for high-level vision tasks such as image editing.
TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video
Much of the previous research on handgun detection is based on static image detectors, leaving aside valuable temporal information that could be used to improve object detection in videos.
Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image Captioning
csalt-research/perturb-predict-paraphrase
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IJCAI 2021
The original algorithm relies on computationally expensive data augmentation steps that involve perturbing the raw images and computing features for each perturbed image.
