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.

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Papers

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Showing 8251–8300 of 8378 papers

TitleStatusHype
Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees—0
What's in a Question: Using Visual Questions as a Form of SupervisionCode0
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks—0
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing—0
Partial Face Detection in the Mobile Domain—0
Smart Augmentation - Learning an Optimal Data Augmentation Strategy—0
SMILES Enumeration as Data Augmentation for Neural Network Modeling of MoleculesCode0
Inference of epidemiological parameters from household stratified data—0
I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation—0
Multilevel Context Representation for Improving Object Recognition—0
Global and Local Information Based Deep Network for Skin Lesion Segmentation—0
A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets—0
Prostate Cancer Diagnosis using Deep Learning with 3D Multiparametric MRI—0
Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis—0
TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process—0
Learning Discrete Representations via Information Maximizing Self-Augmented TrainingCode0
Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation—0
Effective face landmark localization via single deep network—0
Video Salient Object Detection via Fully Convolutional Networks—0
Pooling Facial Segments to Face: The Shallow and Deep Ends—0
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound ClassificationCode0
Bayesian Non-Homogeneous Markov Models via Polya-Gamma Data Augmentation with Applications to Rainfall Modeling—0
Deep Learning for Logo Recognition—0
AENet: Learning Deep Audio Features for Video AnalysisCode0
AGA: Attribute Guided AugmentationCode0
Food Image Recognition by Using Convolutional Neural Networks (CNNs)Code0
Breast Mass Classification from Mammograms using Deep Convolutional Neural NetworksCode0
Towards Robust Deep Neural Networks with BANG—0
Japanese Text Normalization with Encoder-Decoder Model—0
Hyperspectral CNN Classification with Limited Training Samples—0
Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation—0
AFFACT - Alignment-Free Facial Attribute Classification Technique—0
S3Pool: Pooling with Stochastic Spatial SamplingCode0
Dynamic Attention-controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-set Sample Weighting—0
Generalisation and Sharing in Triplet Convnets for Sketch based Visual Search—0
Automatic recognition of child speech for robotic applications in noisy environments—0
Variational Bayes In Private Settings (VIPS)Code0
A data augmentation methodology for training machine/deep learning gait recognition algorithms—0
From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips—0
Deep Fruit Detection in Orchards—0
Multiple Instance Learning Convolutional Neural Networks for Object Recognition—0
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite-Sum StructureCode0
X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets—0
A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image—0
Understanding data augmentation for classification: when to warp?—0
Learning Bayesian Networks with Incomplete Data by Augmentation—0
Softplus Regressions and Convex Polytopes—0
CrowdNet: A Deep Convolutional Network for Dense Crowd CountingCode0
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound ClassificationCode0
Generating Synthetic Data for Text RecognitionCode0
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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