SOTAVerified

Data Augmentation

Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Data augmentation not only helps to grow the dataset but it also increases the diversity of the dataset. When training machine learning models, data augmentation acts as a regularizer and helps to avoid overfitting.

Data augmentation techniques have been found useful in domains like NLP and computer vision. In computer vision, transformations like cropping, flipping, and rotation are used. In NLP, data augmentation techniques can include swapping, deletion, random insertion, among others.

Further readings:

( Image credit: Albumentations )

Papers

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Showing 7951–8000 of 8378 papers

TitleStatusHype
Albumentations: fast and flexible image augmentationsCode0
Déjà Vu: an empirical evaluation of the memorization properties of ConvNets—0
Style Augmentation: Data Augmentation via Style RandomizationCode0
Adapting Semantic Segmentation Models for Changes in Illumination and Camera Perspective—0
Sparse Label Smoothing Regularization for Person Re-IdentificationCode0
SiftingGAN: Generating and Sifting Labeled Samples to Improve the Remote Sensing Image Scene Classification Baseline in vitroCode0
Synthetic Occlusion Augmentation with Volumetric Heatmaps for the 2018 ECCV PoseTrack Challenge on 3D Human Pose EstimationCode0
Label Denoising with Large Ensembles of Heterogeneous Neural Networks—0
Capsule Deep Neural Network for Recognition of Historical Graffiti Handwriting—0
Data Augmentation for Spoken Language Understanding via Joint Variational Generation—0
MixUp as Locally Linear Out-Of-Manifold RegularizationCode0
Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning—0
Learning Invariances for Policy GeneralizationCode0
On the Importance of Visual Context for Data Augmentation in Scene Understanding—0
Data Augmentation for Skin Lesion AnalysisCode0
Aesthetic Discrimination of Graph LayoutsCode0
Data Augmentation for Neural Online Chat Response Selection—0
AugGAN: Cross Domain Adaptation with GAN-based Data Augmentation—0
Multi-object Tracking with Neural Gating Using Bilinear LSTM—0
Generative Adversarial Network with Spatial Attention for Face Attribute EditingCode0
Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti—0
The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments—0
DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime ClassificationCode0
How Robust is 3D Human Pose Estimation to Occlusion?Code0
Targeted Nonlinear Adversarial Perturbations in Images and VideosCode0
How many labeled license plates are needed?—0
Atherosclerotic carotid plaques on panoramic imaging: an automatic detection using deep learning with small dataset—0
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN—0
SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation—0
Reducing Gender Bias in Abusive Language Detection—0
In Defense of Single-column Networks for Crowd Counting—0
Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks—0
Egocentric Gesture Recognition for Head-Mounted AR devices—0
Learning Invariances using the Marginal Likelihood—0
Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection—0
GestureGAN for Hand Gesture-to-Gesture Translation in the WildCode0
Automatic Airway Segmentation in chest CT using Convolutional Neural Networks—0
Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks—0
Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer—0
A Semi-Supervised Data Augmentation Approach using 3D Graphical EnginesCode0
Data augmentation using synthetic data for time series classification with deep residual networksCode0
ODSQA: Open-domain Spoken Question Answering DatasetCode0
Normalization Before Shaking Toward Learning Symmetrically Distributed Representation Without Margin in Speech Emotion Recognition—0
Weather Classification: A new multi-class dataset, data augmentation approach and comprehensive evaluations of Convolutional Neural Networks—0
Identifying Aggression and Toxicity in Comments using Capsule Network—0
Data Augmentation for Robust Keyword Spotting under Playback Interference—0
Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling—0
Aggression Identification Using Deep Learning and Data AugmentationCode0
Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers—0
ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary NetworksCode0
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All datasetsImageNetCIFAR-10GA1457

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeiT-B (+MixPro)Accuracy (%)82.9—Unverified
2ResNet-200 (DeepAA)Accuracy (%)81.32—Unverified
3DeiT-S (+MixPro)Accuracy (%)81.3—Unverified
4ResNet-200 (Fast AA)Accuracy (%)80.6—Unverified
5ResNet-200 (UA)Accuracy (%)80.4—Unverified
6ResNet-200 (AA)Accuracy (%)80—Unverified
7ResNet-50 (DeepAA)Accuracy (%)78.3—Unverified
8ResNet-50 (TA wide)Accuracy (%)78.07—Unverified
9ResNet-50 (LoRot-E)Accuracy (%)77.72—Unverified
10ResNet-50 (LoRot-I)Accuracy (%)77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1WideResNet-40-2 (Faster AA)Percentage error3.7—Unverified
2Shake-Shake (26 2×32d) (Faster AA)Percentage error2.7—Unverified
3WideResNet-28-10 (Faster AA)Percentage error2.6—Unverified
4Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×96d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified