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 1651–1700 of 8378 papers

TitleStatusHype
Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation—0
3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation From Single Depth Images—0
Accuracy Improvement for Fully Convolutional Networks via Selective Augmentation with Applications to Electrocardiogram Data—0
Data Augmentation for Text-based Person Retrieval Using Large Language Models—0
Accounting for Variance in Machine Learning Benchmarks—0
Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context—0
Analysis of Data Augmentation Methods for Low-Resource Maltese ASR—0
A Data Perspective on Enhanced Identity Preservation for Diffusion Personalization—0
Augmenting Character Designers Creativity Using Generative Adversarial Networks—0
Data Augmentation For Small Object using Fast AutoAugment—0
Data Augmentation for Spoken Language Understanding via Joint Variational Generation—0
Learning to Augment: Hallucinating Data for Domain Generalized Segmentation—0
A Machine Learning Enhanced Approach for Automated Sunquake Detection in Acoustic Emission Maps—0
A Machine Learning Approach to Assess Student Group Collaboration Using Individual Level Behavioral Cues—0
A Data-efficient Framework for Robotics Large-scale LiDAR Scene Parsing—0
Data Augmentation for Text Generation Without Any Augmented Data—0
AlzhiNet: Traversing from 2DCNN to 3DCNN, Towards Early Detection and Diagnosis of Alzheimer's Disease—0
Augmented Parallel-Pyramid Net for Attention Guided Pose-Estimation—0
A Data-Efficient Deep Learning Based Smartphone Application For Detection Of Pulmonary Diseases Using Chest X-rays—0
Alzheimer's Dementia Detection Using Perplexity from Paired Large Language Models—0
Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping—0
Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design—0
Augmented Data as an Auxiliary Plug-in Towards Categorization of Crowdsourced Heritage Data—0
A Data-Driven Predictive Analysis on Cyber Security Threats with Key Risk Factors—0
Augmented Cyclic Consistency Regularization for Unpaired Image-to-Image Translation—0
Augmented Bio-SBERT: Improving Performance for Pairwise Sentence Tasks in Bio-medical Domain—0
ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms—0
Accessibility Considerations in the Development of an AI Action Plan—0
Data Augmentation for Sign Language Gloss Translation—0
Augment Before Copy-Paste: Data and Memory Efficiency-Oriented Instance Segmentation Framework for Sport-scenes—0
A Data-Driven Analysis of Robust Automatic Piano Transcription—0
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation—0
Augmentation through Laundering Attacks for Audio Spoof Detection—0
Alternative Data Augmentation for Industrial Monitoring using Adversarial Learning—0
A data-centric approach to class-specific bias in image data augmentation—0
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset—0
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices—0
Augmentation Scheme for Dealing with Imbalanced Network Traffic Classification Using Deep Learning—0
Augmentation Policy Generation for Image Classification Using Large Language Models—0
ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification—0
Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network—0
Data Augmentation for the Post-Stroke Speech Transcription (PSST) Challenge: Sometimes Less Is More—0
A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems—0
Augmentation of EEG and ECG Time Series for Deep Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates—0
Data augmentation and refinement for recommender system: A semi-supervised approach using maximum margin matrix factorization—0
Augmentation of Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models—0
A low-cost PPG sensor-based empirical study on healthy aging based on changes in PPG morphology—0
A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection—0
Data Augmentation for Robust Keyword Spotting under Playback Interference—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified