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 7251–7300 of 8378 papers

TitleStatusHype
SimEx: Express Prediction of Inter-dataset Similarity by a Fleet of Autoencoders—0
GridMask Data AugmentationCode1
Semi-supervised learning method based on predefined evenly-distributed class centroids—0
Rethinking Curriculum Learning with Incremental Labels and Adaptive Compensation—0
Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems—0
Bayesian Semi-supervised Multi-category Classification under Nonparanormality—0
Neural Network Tomography—0
CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency—0
RMNv2: Reduced Mobilenet V2 for CIFAR10—0
A Robust Pose Transformational GAN for Pose Guided Person Image Synthesis—0
HandAugment: A Simple Data Augmentation Method for Depth-Based 3D Hand Pose EstimationCode1
NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture SearchCode1
Attention based on-device streaming speech recognition with large speech corpus—0
aiai at the FinSBD-2 Task: Sentence, list and Item Boundary Detection and Items classification of Financial Texts Using Data Augmentation and Attention—0
Conditional Augmentation for Generative Modeling—0
Analytical Moment Regularizer for Training Robust Networks—0
Machine Learning based Post Processing Artifact Reduction in HEVC Intra Coding—0
PMC-GANs: Generating Multi-Scale High-Quality Pedestrian with Multimodal Cascaded GANs—0
FLAT: Few-Shot Learning via Autoencoding Transformation Regularizers—0
Tha3aroon at NSURL-2019 Task 8: Semantic Question Similarity in ArabicCode1
DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation—0
A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts—0
Effective Data Augmentation with Multi-Domain Learning GANs—0
Adversarial AutoAugment—0
Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks—0
CNN-generated images are surprisingly easy to spot... for nowCode1
end-to-end training of a large vocabulary end-to-end speech recognition system—0
A Deep Learning Model for Chilean Bills ClassificationCode0
AEGR: A simple approach to gradient reversal in autoencoders for network anomaly detection—0
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection—0
The State of Knowledge Distillation for ClassificationCode0
Triple Generative Adversarial NetworksCode0
LS-Net: Fast Single-Shot Line-Segment Detector—0
Scale-wise Convolution for Image RestorationCode1
Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems—0
LSTM-TDNN with convolutional front-end for Dialect Identification in the 2019 Multi-Genre Broadcast Challenge—0
Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object DetectionCode0
Simulating Content Consistent Vehicle Datasets with Attribute DescentCode1
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking—0
Data augmentation approaches for improving animal audio classification—0
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration—0
Joint Learning of Generative Translator and Classifier for Visually Similar Classes—0
Training without training data: Improving the generalizability of automated medical abbreviation disambiguation—0
The Wasserstein-Fourier Distance for Stationary Time SeriesCode0
Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation—0
Multimodal Self-Supervised Learning for Medical Image Analysis—0
Parting with Illusions about Deep Active Learning—0
Audiogmenter: a MATLAB Toolbox for Audio Data AugmentationCode0
Bias Remediation in Driver Drowsiness Detection systems using Generative Adversarial Networks—0
Medication Regimen Extraction From Medical Conversations—0
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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