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 4401–4450 of 8378 papers

TitleStatusHype
Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation—0
Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks—0
HULAT at SemEval-2023 Task 10: Data augmentation for pre-trained transformers applied to the detection of sexism in social mediaCode0
HULAT at SemEval-2023 Task 9: Data augmentation for pre-trained transformers applied to Multilingual Tweet Intimacy AnalysisCode0
Disease Severity Regression with Continuous Data Augmentation—0
Data Augmentation with GAN increases the Performance of Arrhythmia Classification for an Unbalanced Dataset—0
GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification—0
Hybrid machine-learned homogenization: Bayesian data mining and convolutional neural networks—0
Random Teachers are Good TeachersCode0
Deep Regularized Waveform Learning for Beam Prediction With Limited Samples in Non-Cooperative mmWave SystemsCode0
Contrastive Representation Learning for Acoustic Parameter Estimation—0
What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel—0
Data Augmentation for Neural NLP—0
Distilling Calibrated Student from an Uncalibrated Teacher—0
DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition—0
Improving Contextual Spelling Correction by External Acoustics Attention and Semantic Aware Data Augmentation—0
Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Timeseries Data Imputation—0
Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning—0
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset—0
Neural Algorithmic Reasoning with Causal Regularisation—0
DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning ModelsCode0
JNDMix: JND-Based Data Augmentation for No-reference Image Quality Assessment—0
Pseudo Contrastive Learning for Graph-based Semi-supervised Learning—0
VITAL: Vision Transformer Neural Networks for Accurate Smartphone Heterogeneity Resilient Indoor Localization—0
Data Augmentation for Imbalanced RegressionCode0
Incipient Fault Detection in Power Distribution System: A Time-Frequency Embedded Deep Learning Based ApproachCode0
Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition—0
DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure reconstruction from extremely small data sets—0
A Review on Generative Adversarial Networks for Data Augmentation in Person Re-Identification Systems—0
Random Padding Data Augmentation—0
Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation—0
Offline-to-Online Knowledge Distillation for Video Instance Segmentation—0
Qualitative Data Augmentation for Performance Prediction in VLSI circuits—0
Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism—0
How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval—0
A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation—0
READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input NoisesCode0
Enhancing Model Performance in Multilingual Information Retrieval with Comprehensive Data Engineering Techniques—0
BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Prediction—0
Adding Instructions during Pretraining: Effective Way of Controlling Toxicity in Language Models—0
Detection and classification of vocal productions in large scale audio recordingsCode0
Finetuning Is a Surprisingly Effective Domain Adaptation Baseline in Handwriting Recognition—0
Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents—0
PK-ICR: Persona-Knowledge Interactive Context Retrieval for Grounded DialogueCode0
Bag of Tricks for In-Distribution Calibration of Pretrained TransformersCode0
Flag Aggregator: Scalable Distributed Training under Failures and Augmented Losses using Convex OptimizationCode0
Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns—0
Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information—0
Evaluation of Data Augmentation and Loss Functions in Semantic Image Segmentation for Drilling Tool Wear DetectionCode0
Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization—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