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 3451–3500 of 8378 papers

TitleStatusHype
COVID-19 Classification of X-ray Images Using Deep Neural Networks—0
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU—0
Alibaba’s Submission for the WMT 2020 APE Shared Task: Improving Automatic Post-Editing with Pre-trained Conditional Cross-Lingual BERT—0
A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification From Analytical Augmented Sample Moments—0
Accelerating Real-Time Question Answering via Question Generation—0
Counting Fish and Dolphins in Sonar Images Using Deep Learning—0
Countering Adversarial Examples: Combining Input Transformation and Noisy Training—0
Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns—0
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampler—0
Ali-AUG: Innovative Approaches to Labeled Data Augmentation using One-Step Diffusion Model—0
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling—0
A Twitter BERT Approach for Offensive Language Detection in Marathi—0
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation—0
Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition—0
AlexU-BackTranslation-TL at SemEval-2020 Task 12: Improving Offensive Language Detection Using Data Augmentation and Transfer Learning—0
A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networks—0
CATE Estimation With Potential Outcome Imputation From Local Regression—0
Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification—0
ALEM at CASE 2021 Task 1: Multilingual Text Classification on News Articles—0
Counterfactual Data Augmentation improves Factuality of Abstractive Summarization—0
Attention-stacked Generative Adversarial Network (AS-GAN)-empowered Sensor Data Augmentation for Online Monitoring of Manufacturing System—0
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology—0
Attention Speaks Volumes: Localizing and Mitigating Bias in Language Models—0
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images—0
Accelerating Molecular Graph Neural Networks via Knowledge Distillation—0
3D-Aided Data Augmentation for Robust Face Understanding—0
Counterfactual Collaborative Reasoning—0
AttentionMix: Data augmentation method that relies on BERT attention mechanism—0
Could We Generate Cytology Images from Histopathology Images? An Empirical Study—0
Attention Is All You Need For Blind Room Volume Estimation—0
A Large Language Model and Denoising Diffusion Framework for Targeted Design of Microstructures with Commands in Natural Language—0
Co-training and Co-distillation for Quality Improvement and Compression of Language Models—0
Attention-Guided Erasing: A Novel Augmentation Method for Enhancing Downstream Breast Density Classification—0
Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data—0
Attention-GAN for Anomaly Detection: A Cutting-Edge Approach to Cybersecurity Threat Management—0
A Label Propagation Strategy for CutMix in Multi-Label Remote Sensing Image Classification—0
Attention, Filling in The Gaps for Generalization in Routing Problems—0
Corrosion Detection for Industrial Objects: From Multi-Sensor System to 5D Feature Space—0
Correlation Sketches for Approximate Join-Correlation Queries—0
Attention-Enhanced U-Net for Accurate Segmentation of COVID-19 Infected Lung Regions in CT Scans—0
Correlation-Aware Select and Merge Attention for Efficient Fine-Tuning and Context Length Extension—0
Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model—0
Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation—0
Coronary Artery Disease Classification with Different Lesion Degree Ranges based on Deep Learning—0
Attention based on-device streaming speech recognition with large speech corpus—0
Exploring Limits of Diffusion-Synthetic Training with Weakly Supervised Semantic Segmentation—0
Accelerating Ensemble Error Bar Prediction with Single Models Fits—0
CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction—0
Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints—0
CORE: Data Augmentation for Link Prediction via Information Bottleneck—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