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 4751–4800 of 8378 papers

TitleStatusHype
R-Mixup: Riemannian Mixup for Biological Networks—0
RMNv2: Reduced Mobilenet V2 for CIFAR10—0
ROAR: Reinforcing Original to Augmented Data Ratio Dynamics for Wav2Vec2.0 Based ASR—0
OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images—0
Robotic and Generative Adversarial Attacks in Offline Writer-independent Signature Verification—0
Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations—0
Robust 2D/3D Vehicle Parsing in CVIS—0
Robust 3D Object Detection in Cold Weather Conditions—0
Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging—0
Robust and High Performance Face Detector—0
Robust Autonomous Vehicle Pursuit without Expert Steering Labels—0
Robust Calibration For Improved Weather Prediction Under Distributional Shift—0
Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts—0
Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning—0
Robust Deep Multi-modal Learning Based on Gated Information Fusion Network—0
Robust Detection, Association, and Localization of Vehicle Lights: A Context-Based Cascaded CNN Approach and Evaluations—0
Robust Fraud Detection via Supervised Contrastive Learning—0
Robust Handwriting Recognition with Limited and Noisy Data—0
Robustifying deep networks for image segmentation—0
Robustifying Deep Vision Models Through Shape Sensitization—0
Robust Information Retrieval for False Claims with Distracting Entities In Fact Extraction and Verification—0
Robust Learning-Based Incipient Slip Detection using the PapillArray Optical Tactile Sensor for Improved Robotic Gripping—0
Robustly Learning Monotone Generalized Linear Models via Data Augmentation—0
Robust Machine Comprehension Models via Adversarial Training—0
Robust Machine Learning by Transforming and Augmenting Imperfect Training Data—0
Robust Mitosis Detection Using a Cascade Mask-RCNN Approach With Domain-Specific Residual Cycle-GAN Data Augmentation—0
Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets—0
Robustness and Adaptation to Hidden Factors of Variation—0
Robustness and invariance properties of image classifiers—0
Robustness Benchmark of Road User Trajectory Prediction Models for Automated Driving—0
Robustness Enhancement in Neural Networks with Alpha-Stable Training Noise—0
Robustness in Compressed Neural Networks for Object Detection—0
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?—0
Robustness-Reinforced Knowledge Distillation with Correlation Distance and Network Pruning—0
Robustness to Capitalization Errors in Named Entity Recognition—0
Robust Object Classification Approach using Spherical Harmonics—0
Robust pedestrian detection in thermal imagery using synthesized images—0
Robust Pollen Imagery Classification with Generative Modeling and Mixup Training—0
Robust Polyp Detection and Diagnosis through Compositional Prompt-Guided Diffusion Models—0
Robust Prediction of Punctuation and Truecasing for Medical ASR—0
Robust Retinal Vessel Segmentation from a Data Augmentation Perspective—0
Robust sensor fusion against on-vehicle sensor staleness—0
Robust Sentiment Analysis for Low Resource languages Using Data Augmentation Approaches: A Case Study in Marathi—0
Robust soybean seed yield estimation using high-throughput ground robot videos—0
Robust Speaker Recognition with Transformers Using wav2vec 2.0—0
Table Integration in Data Lakes Unleashed: Pairwise Integrability Judgment, Integrable Set Discovery, and Multi-Tuple Conflict Resolution—0
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks—0
Robust Training Using Natural Transformation—0
Robust Training with Data Augmentation for Medical Imaging Classification—0
Robust Trajectory Prediction against Adversarial Attacks—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