Image Augmentation
100 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find Image Augmentation models and implementations
Intel Image Classification
Most implemented papers
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
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.
Deep PCB To COCO Convertor
It has 1500 image pairs.
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.
Three things everyone should know to improve object retrieval
The objective of this work is object retrieval in large scale image datasets, where the object is specified by an image query and retrieval should be immediate at run time in the manner of Video Google [28].
Data Augmentation via Levy Processes
The framework imagines data as being drawn from a slice of a Levy process.
Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and Estimation
Convolutional neural networks (CNNs) have yielded the excellent performance in a variety of computer vision tasks, where CNNs typically adopt a similar structure consisting of convolution layers, pooling layers and fully connected layers.
Learning to Compose Domain-Specific Transformations for Data Augmentation
HazyResearch/tanda
•
•
NeurIPS 2017
Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels.
Parallel Grid Pooling for Data Augmentation
Convolutional neural network (CNN) architectures utilize downsampling layers, which restrict the subsequent layers to learn spatially invariant features while reducing computational costs.
Improved Mixed-Example Data Augmentation
ceciliaresearch/MixedExample
•
•
In order to reduce overfitting, neural networks are typically trained with data augmentation, the practice of artificially generating additional training data via label-preserving transformations of existing training examples.
Data Augmentation using Random Image Cropping and Patching for Deep CNNs
jackryo/ricap
•
•
We also confirmed that deep CNNs with RICAP achieve better results on classification tasks using CIFAR-100 and ImageNet and an image-caption retrieval task using Microsoft COCO.
