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
Most implemented papers
An Open-source Tool for Hyperspectral Image Augmentation in Tensorflow
mabdelhack/hyperspectral_image_generator
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Tensorflow tool allows for rapid prototyping and testing of deep learning models, however, its built-in image generator is designed to handle a maximum of four spectral channels.
Visual Question Generation from Radiology Images
sarrouti/vqgr
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WS 2020
Visual Question Generation (VQG), the task of generating a question based on image contents, is an increasingly important area that combines natural language processing and computer vision.
Anatomical Data Augmentation via Fluid-based Image Registration
uncbiag/easyreg
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We introduce a fluid-based image augmentation method for medical image analysis.
A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
kornia/kornia
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This work presents Kornia, an open source computer vision library built upon a set of differentiable routines and modules that aims to solve generic computer vision problems.
Data Augmentation Based Malware Detection using Convolutional Neural Networks
The main contributions of the paper's model structure consist of three components, including image generation from malware samples, image augmentation, and the last one is classifying the malware families by using a convolutional neural network model.
A Comparative Study on Efficiencies of Variants of Convolutional Neural Networks based on Image Classification Task
Deep neural networks demonstrate to have a high performance on image classification tasks while being more difficult to train.
Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development.
Differentiable Data Augmentation with Kornia
In this paper we present a review of the Kornia differentiable data augmentation (DDA) module for both for spatial (2D) and volumetric (3D) tensors.
Application of Facial Recognition using Convolutional Neural Networks for Entry Access Control
More specifically, the paper focuses on solving the supervised classification problem of taking images of people as input and classifying the person in the image as one of the authors or not.
Sparse Signal Models for Data Augmentation in Deep Learning ATR
Automatic Target Recognition (ATR) algorithms classify a given Synthetic Aperture Radar (SAR) image into one of the known target classes using a set of training images available for each class.
