Image Augmentation

95 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 )

Libraries

Use these libraries to find Image Augmentation models and implementations
2 papers
37,721
2 papers
15,282
2 papers
9,242
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Most implemented papers

Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

denisyarats/drq ICLR 2021

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

ZohebAbai/Tiny-ImageNet-Challenge 23 Apr 2019

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 14 May 2019

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 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 27 Apr 2020

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 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 18 Nov 2020

Pavement condition evaluation is essential to time the preventative or rehabilitative actions and control distress propagation.

Salient Objects in Clutter

dengpingfan/sodbenchmark 7 May 2021

This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets.

Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input

dreamflake/odi 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.