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 6301–6350 of 8378 papers

TitleStatusHype
Contrastive Weighted Learning for Near-Infrared Gaze Estimation—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
Controllable and Diverse Data Augmentation with Large Language Model for Low-Resource Open-Domain Dialogue Generation—0
Controllable and Efficient Multi-Class Pathology Nuclei Data Augmentation using Text-Conditioned Diffusion Models—0
Controllable Data Augmentation for Context-Dependent Text-to-SQL—0
Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies—0
Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathy—0
Controllable Text Simplification with Explicit Paraphrasing—0
Controllable Top-down Feature Transformer—0
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents—0
ControlMath: Controllable Data Generation Promotes Math Generalist Models—0
ControlTac: Force- and Position-Controlled Tactile Data Augmentation with a Single Reference Image—0
Conversation AI Dialog for Medicare powered by Finetuning and Retrieval Augmented Generation—0
Conversational Recommendation as Retrieval: A Simple, Strong Baseline—0
Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy—0
Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation—0
Convolutional neural network for Lyman break galaxies classification and redshift regression in DESI (Dark Energy Spectroscopic Instrument)—0
Convolutional Neural Networks for Automated Cellular Automaton Classification—0
Convolutional Neural Networks for Automatic Meter Reading—0
Convolutional Neural Networks for Font Classification—0
Domain specific cues improve robustness of deep learning based segmentation of ct volumes—0
Coordination Generation via Synchronized Text-Infilling—0
COPD-FlowNet: Elevating Non-invasive COPD Diagnosis with CFD Simulations—0
CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction—0
CopyPaste: An Augmentation Method for Speech Emotion Recognition—0
CORE: Data Augmentation for Link Prediction via Information Bottleneck—0
CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction—0
Coronary Artery Disease Classification with Different Lesion Degree Ranges based on Deep Learning—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
Correlation Sketches for Approximate Join-Correlation Queries—0
Corrosion Detection for Industrial Objects: From Multi-Sensor System to 5D Feature Space—0
Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data—0
Co-training and Co-distillation for Quality Improvement and Compression of Language Models—0
Could We Generate Cytology Images from Histopathology Images? An Empirical Study—0
Counterfactual Collaborative Reasoning—0
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology—0
Counterfactual Data Augmentation improves Factuality of Abstractive Summarization—0
CATE Estimation With Potential Outcome Imputation From Local Regression—0
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation—0
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling—0
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampler—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
COVID-19 Classification of X-ray Images Using Deep Neural Networks—0
CoVid-19 Detection leveraging Vision Transformers and Explainable AI—0
CoViews: Adaptive Augmentation Using Cooperative Views for Enhanced Contrastive Learning—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
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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