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 6551–6600 of 8378 papers

TitleStatusHype
Robustness in Compressed Neural Networks for Object Detection—0
Enhancing Audio Augmentation Methods with Consistency Learning—0
The Role of the Input in Natural Language Video Description—0
Tracking e-cigarette warning label compliance on Instagram with deep learning—0
Deep Learning Based Walking Tasks Classification in Older Adults using fNIRS—0
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
Functional Space Analysis of Local GAN Convergence—0
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning—0
Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR—0
Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose—0
Self-Supervised Deep Graph Embedding with High-Order Information Fusion for Community Discovery—0
Multispectral Object Detection with Deep Learning—0
On the Reproducibility of Neural Network Predictions—0
Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR imagesCode0
Robust pedestrian detection in thermal imagery using synthesized images—0
Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation—0
On Robustness of Neural Semantic Parsers—0
Interpretable COVID-19 Chest X-Ray Classification via Orthogonality Constraint—0
Neural Data Augmentation via Example ExtrapolationCode0
Ultrasound Image Classification using ACGAN with Small Training DatasetCode0
[Re] Warm-Starting Neural Network TrainingCode0
ObjectAug: Object-level Data Augmentation for Semantic Image Segmentation—0
Speech Enhancement for Wake-Up-Word detection in Voice Assistants—0
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification—0
Enhancing Sequence-to-Sequence Neural Lemmatization with External ResourcesCode0
On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of micro-PCBsCode0
A Multi-Scale Conditional Deep Model for Tumor Cell Ratio Counting—0
El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing—0
Facilitating Terminology Translation with Target Lemma AnnotationsCode0
Mask-based Data Augmentation for Semi-supervised Semantic Segmentation—0
Spatio-temporal Data Augmentation for Visual Surveillance—0
A Pressure Ulcer Care System For Remote Medical Assistance: Residual U-Net with an Attention Model Based for Wound Area Segmentation—0
Rethinking Domain Generalization Baselines—0
Automatic Cerebral Vessel Extraction in TOF-MRA Using Deep Learning—0
DataLoc+: A Data Augmentation Technique for Machine Learning in Room-Level Indoor Localization—0
Distilling Large Language Models into Tiny and Effective Students using pQRNN—0
Class balanced underwater object detection dataset generated by class-wise style augmentation—0
Extensive Studies of the Neutron Star Equation of State from the Deep Learning Inference with the Observational Data Augmentation—0
Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transferCode0
An Empirical Study and Analysis on Open-Set Semi-Supervised Learning—0
On Data-Augmentation and Consistency-Based Semi-Supervised Learning—0
Learning Visual Representations with Optimum-Path Forest and its Applications to Barrett's Esophagus and Adenocarcinoma Diagnosis—0
Removing Undesirable Feature Contributions Using Out-of-Distribution DataCode0
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors—0
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving—0
Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation—0
Improve Global Glomerulosclerosis Classification with Imbalanced Data using CircleMix AugmentationCode0
Motion-Based Handwriting RecognitionCode0
Text Augmentation in a Multi-Task View—0
Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records—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