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 5651–5700 of 8378 papers

TitleStatusHype
Semi-Supervised Few-Shot Learning via Multi-Factor ClusteringCode0
Evaluating Deep Music Generation Methods Using Data Augmentation—0
Towards Robustness of Neural Networks—0
Uncertainty Detection and Reduction in Neural Decoding of EEG SignalsCode0
LINDA: Unsupervised Learning to Interpolate in Natural Language Processing—0
Generation of Synthetic Rat Brain MRI scans with a 3D Enhanced Alpha-GAN—0
Acoustic scene classification using auditory datasetsCode0
TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue Modeling on Spoken Conversations—0
Self-Supervised Graph Representation Learning for Neuronal Morphologies—0
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition—0
Improving Robustness with Image Filtering—0
Crash Data Augmentation Using Conditional Generative Adversarial Networks (CGAN) for Improving Safety Performance Functions—0
PRONTO: Preamble Overhead Reduction with Neural Networks for Coarse SynchronizationCode0
Data Augmentation for Mental Health Classification on Social Media—0
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey—0
Data Augmentation through Expert-guided Symmetry Detection to Improve Performance in Offline Reinforcement LearningCode0
Multiple Instance Learning for Brain Tumor Detection from Magnetic Resonance Spectroscopy Data—0
How to augment your ViTs? Consistency loss and StyleAug, a random style transfer augmentation—0
Adapting Document-Grounded Dialog Systems to Spoken Conversations using Data Augmentation and a Noisy Channel ModelCode0
Bioacoustic Event Detection with prototypical networks and data augmentation—0
Mitigating the Bias of Centered Objects in Common Datasets—0
ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification—0
Improved YOLOv5 network for real-time multi-scale traffic sign detectionCode0
Invariance Through Latent Alignment—0
DG2: Data Augmentation Through Document Grounded Dialogue Generation—0
Improving COVID-19 CXR Detection with Synthetic Data Augmentation—0
ImportantAug: a data augmentation agent for speechCode0
Handwritten text generation and strikethrough characters augmentation—0
Maximum Bayes Smatch Ensemble Distillation for AMR ParsingCode0
PM-MMUT: Boosted Phone-Mask Data Augmentation using Multi-Modeling Unit Training for Phonetic-Reduction-Robust E2E Speech Recognition—0
Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-trainingCode0
Improving Logical-Level Natural Language Generation with Topic-Conditioned Data Augmentation and Logical Form Generation—0
Injecting Numerical Reasoning Skills into Knowledge Base Question Answering ModelsCode0
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses—0
Learning Contraction Policies from Offline Data—0
Automated assessment of disease severity of COVID-19 using artificial intelligence with synthetic chest CT—0
On Automatic Data Augmentation for 3D Point Cloud ClassificationCode0
Learning to Learn Transferable AttackCode0
Robust Information Retrieval for False Claims with Distracting Entities In Fact Extraction and Verification—0
Image-to-Image Translation-based Data Augmentation for Robust EV Charging Inlet DetectionCode0
Layer-Parallel Training of Residual Networks with Auxiliary-Variable Networks—0
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection—0
InvGAN: Invertible GANs—0
Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularization—0
A systematic approach to random data augmentation on graph neural networks—0
SIRfyN: Single Image Relighting from your Neighbors—0
ViewCLR: Learning Self-supervised Video Representation for Unseen Viewpoints—0
Generative Adversarial Networks for Labeled Acceleration Data Augmentation for Structural Damage Detection—0
Physics guided deep learning generative models for crystal materials discovery—0
Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization—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