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

Showing 80018050 of 8378 papers

TitleStatusHype
Back-Translation-Style Data Augmentation for End-to-End ASR0
Acoustic and Textual Data Augmentation for Improved ASR of Code-Switching Speech0
Characters Detection on Namecard with faster RCNN0
Deep Learning Methods and Applications for Region of Interest Detection in Dermoscopic Images0
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks0
ISIC 2017 Skin Lesion Segmentation Using Deep Encoder-Decoder Network0
MVDepthNet: Real-time Multiview Depth Estimation Neural NetworkCode0
Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation0
Automatic Speech Recognition for Humanitarian Applications in Somali0
Conditional Infilling GANs for Data Augmentation in Mammogram ClassificationCode0
Monocular Object Orientation Estimation using Riemannian Regression and Classification NetworksCode0
Bio-Measurements Estimation and Support in Knee Recovery through Machine Learning0
Modeling Visual Context is Key to Augmenting Object Detection DatasetsCode0
Learning Noise-Invariant Representations for Robust Speech Recognition0
Robust Deep Multi-modal Learning Based on Gated Information Fusion Network0
A Dataset of Laryngeal Endoscopic Images with Comparative Study on Convolution Neural Network Based Semantic SegmentationCode0
Semi-supervised Feature Learning For Improving Writer Identification0
Deep neural network ensemble by data augmentation and bagging for skin lesion classification0
Neural Networks Regularization Through Representation LearningCode0
Deep semi-supervised segmentation with weight-averaged consistency targets0
Subsampled Turbulence Removal Network0
Hydranet: Data Augmentation for Regression Neural Networks0
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations0
High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks0
Data Augmentation for Detection of Architectural Distortion in Digital Mammography using Deep Learning Approach0
Sequence-to-Sequence Data Augmentation for Dialogue Language UnderstandingCode0
HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI0
Learning under selective labels in the presence of expert consistency0
Semantically Equivalent Adversarial Rules for Debugging NLP modelsCode0
Dependent Relational Gamma Process Models for Longitudinal Networks0
CMMC-BDRC Solution to the NLP-TEA-2018 Chinese Grammatical Error Diagnosis Task0
Shape-from-Mask: A Deep Learning Based Human Body Shape Reconstruction from Binary Mask Images0
Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks0
HitNet: a neural network with capsules embedded in a Hit-or-Miss layer, extended with hybrid data augmentation and ghost capsulesCode0
Learning to Evaluate Image CaptioningCode0
Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention0
A Study of Enhancement, Augmentation, and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition0
Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network0
Semantically Selective Augmentation for Deep Compact Person Re-Identification0
Data augmentation instead of explicit regularizationCode0
Transformationally Identical and Invariant Convolutional Neural Networks through Symmetric Element Operators0
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data0
A Systematic Evaluation of Recent Deep Learning Architectures for Fine-Grained Vehicle ClassificationCode0
Findings of the Second Workshop on Neural Machine Translation and Generation0
Speaker-Follower Models for Vision-and-Language NavigationCode0
Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples0
Training Augmentation with Adversarial Examples for Robust Speech Recognition0
Study and development of a Computer-Aided Diagnosis system for classification of chest x-ray images using convolutional neural networks pre-trained for ImageNet and data augmentation0
DA-GAN: Instance-Level Image Translation by Deep Attention Generative Adversarial Networks0
Lyb3b at SemEval-2018 Task 11: Machine Comprehension Task using Deep Learning Models0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeiT-B (+MixPro)Accuracy (%)82.9Unverified
2ResNet-200 (DeepAA)Accuracy (%)81.32Unverified
3DeiT-S (+MixPro)Accuracy (%)81.3Unverified
4ResNet-200 (Fast AA)Accuracy (%)80.6Unverified
5ResNet-200 (UA)Accuracy (%)80.4Unverified
6ResNet-200 (AA)Accuracy (%)80Unverified
7ResNet-50 (DeepAA)Accuracy (%)78.3Unverified
8ResNet-50 (TA wide)Accuracy (%)78.07Unverified
9ResNet-50 (LoRot-E)Accuracy (%)77.72Unverified
10ResNet-50 (LoRot-I)Accuracy (%)77.71Unverified
#ModelMetricClaimedVerifiedStatus
1WideResNet-40-2 (Faster AA)Percentage error3.7Unverified
2Shake-Shake (26 2×32d) (Faster AA)Percentage error2.7Unverified
3WideResNet-28-10 (Faster AA)Percentage error2.6Unverified
4Shake-Shake (26 2×112d) (Faster AA)Percentage error2Unverified
5Shake-Shake (26 2×96d) (Faster AA)Percentage error2Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7Unverified
2PaCMAPClassification Accuracy85.3Unverified
3hNNEClassification Accuracy77.4Unverified
4TopoAEClassification Accuracy74.6Unverified