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 1801–1850 of 8378 papers

TitleStatusHype
CopyPaste: An Augmentation Method for Speech Emotion Recognition—0
Attacking Voice Anonymization Systems with Augmented Feature and Speaker Identity Difference—0
CORE: Data Augmentation for Link Prediction via Information Bottleneck—0
A Time-Series Data Augmentation Model through Diffusion and Transformer Integration—0
Domain specific cues improve robustness of deep learning based segmentation of ct volumes—0
CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction—0
Dialog State Tracking with Reinforced Data Augmentation—0
Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation—0
Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model—0
Correlation-Aware Select and Merge Attention for Efficient Fine-Tuning and Context Length Extension—0
Data for free: Fewer-shot algorithm learning with parametricity data augmentation—0
Corrosion Detection for Industrial Objects: From Multi-Sensor System to 5D Feature Space—0
DataLoc+: A Data Augmentation Technique for Machine Learning in Room-Level Indoor Localization—0
Attention, Filling in The Gaps for Generalization in Routing Problems—0
DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure reconstruction from extremely small data sets—0
Deep COVID-19 Forecasting for Multiple States with Data Augmentation—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
Could We Generate Cytology Images from Histopathology Images? An Empirical Study—0
Convolutional Neural Networks for Font Classification—0
AttentionMix: Data augmentation method that relies on BERT attention mechanism—0
Counterfactual Collaborative Reasoning—0
Convolutional Neural Networks for Automatic Meter Reading—0
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology—0
A Three Step Training Approach with Data Augmentation for Morphological Inflection—0
Counterfactual Data Augmentation improves Factuality of Abstractive Summarization—0
Convolutional Neural Networks for Automated Cellular Automaton Classification—0
Convolutional neural network for Lyman break galaxies classification and redshift regression in DESI (Dark Energy Spectroscopic Instrument)—0
Atherosclerotic carotid plaques on panoramic imaging: an automatic detection using deep learning with small dataset—0
CATE Estimation With Potential Outcome Imputation From Local Regression—0
Adaptive Spatial Augmentation for Semi-supervised Semantic Segmentation—0
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation—0
A Twitter BERT Approach for Offensive Language Detection in Marathi—0
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling—0
Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation—0
Counter-GAP: Counterfactual Bias Evaluation through Gendered Ambiguous Pronouns—0
Countering Adversarial Examples: Combining Input Transformation and Noisy Training—0
Counting Fish and Dolphins in Sonar Images Using Deep Learning—0
A Theory of PAC Learnability under Transformation Invariances—0
A Target-Aware Analysis of Data Augmentation for Hate Speech Detection—0
CoVid-19 Detection leveraging Vision Transformers and Explainable AI—0
AUC-mixup: Deep AUC Maximization with Mixup—0
A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling—0
Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data—0
Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy—0
Adaptive Regularization of Labels—0
A Causal View on Robustness of Neural Networks—0
CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose—0
Crash Data Augmentation Using Conditional Generative Adversarial Networks (CGAN) for Improving Safety Performance Functions—0
Conversational Recommendation as Retrieval: A Simple, Strong Baseline—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