Image Augmentation
96 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
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
We propose a simple data augmentation technique that can be applied to standard model-free reinforcement learning algorithms, enabling robust learning directly from pixels without the need for auxiliary losses or pre-training.
DenseNet Models for Tiny ImageNet Classification
The architecture of the networks is designed based on the image resolution of this specific dataset and by calculating the Receptive Field of the convolution layers.
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules
arcelien/pba
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A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations.
Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition
Canjie-Luo/Text-Image-Augmentation
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CVPR 2020
An agent network learns from the output of the recognition network and controls the fiducial points to generate more proper training samples for the recognition network.
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents
DevashishPrasad/CascadeTabNet
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In this paper, we present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model.
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
MegviiDetection/FSCE
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CVPR 2021
We present Few-Shot object detection via Contrastive proposals Encoding (FSCE), a simple yet effective approach to learning contrastive-aware object proposal encodings that facilitate the classification of detected objects.
An Efficient and Scalable Deep Learning Approach for Road Damage Detection
mahdi65/roadDamageDetection2020
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Pavement condition evaluation is essential to time the preventative or rehabilitative actions and control distress propagation.
Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models
frankkramer-lab/riadd.aucmedi
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Preventable or undiagnosed visual impairment and blindness affect billion of people worldwide.
Salient Objects in Clutter
This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets.
Detectron2 Object Detection & Manipulating Images using Cartoonization
avs-abhishek123/Detectron2
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International Journal of Engineering Research & Technology (IJERT) 2021
In today's world, there is a rapid increase in the autonomous vehicle.
