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:

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Papers

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Showing 8101–8150 of 8378 papers

TitleStatusHype
One Kernel to Solve Nearly Everything: Unified 3D Binary Convolutions for Image Analysis—0
EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field ImagesCode0
On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks—0
Impact of ultrasound image reconstruction method on breast lesion classification with neural transfer learning—0
Semi-Supervised Deep Metrics for Image Registration—0
Generative Adversarial Learning for Spectrum Sensing—0
Recognizing Challenging Handwritten Annotations with Fully Convolutional Networks—0
Parallel Grid Pooling for Data AugmentationCode0
Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection—0
Multi-Modal Data Augmentation for End-to-End ASR—0
Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation—0
Learning the Localization Function: Machine Learning Approach to Fingerprinting Localization—0
Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks—0
A Kernel Theory of Modern Data Augmentation—0
Large Margin Deep Networks for ClassificationCode0
A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images—0
Differential Expression Analysis of Dynamical Sequencing Count Data with a Gamma Markov Chain—0
New Results on Multi-Step Traffic Flow Prediction—0
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification—0
Deep Bayesian Active Semi-Supervised LearningCode0
Improving Low Resource Machine Translation using Morphological Glosses (Non-archival Extended Abstract)—0
Matching Convolutional Neural Networks without Priors about DataCode0
OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers using Piecewise Convolutional Neural Networks—0
Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees—0
Sensitivity and Generalization in Neural Networks: an Empirical Study—0
Improved Techniques For Weakly-Supervised Object Localization—0
Conditional Adversarial Synthesis of 3D Facial Action Units—0
Do deep nets really need weight decay and dropout?Code0
DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials)—0
Efficient Gaussian Process Classification Using Polya-Gamma Data AugmentationCode0
CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation—0
Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR ImagesCode0
Predicting Adversarial Examples with High Confidence—0
Tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction—0
Full-Frame Scene Coordinate Regression for Image-Based Localization—0
A Two-Stage Method for Text Line Detection in Historical DocumentsCode0
Rollable Latent Space for Azimuth Invariant SAR Target Recognition—0
Data Augmentation of Railway Images for Track Inspection—0
DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion—0
tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid FlowCode0
Visual Data Augmentation through Learning—0
3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies—0
Feature Space Transfer for Data Augmentation—0
Inferring a Third Spatial Dimension from 2D Histological Images—0
Data Augmentation for Brain-Computer Interfaces: Analysis on Event-Related Potentials Data—0
Data Augmentation by Pairing Samples for Images Classification—0
SketchyGAN: Towards Diverse and Realistic Sketch to Image SynthesisCode0
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification—0
Anatomical Data Augmentation For CNN based Pixel-wise Classification—0
Building Generalizable Agents with a Realistic and Rich 3D EnvironmentCode0
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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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified