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 2701–2750 of 8378 papers

TitleStatusHype
ARPA: A Novel Hybrid Model for Advancing Visual Word Disambiguation Using Large Language Models and Transformers—0
Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization—0
Advanced Vision Transformers and Open-Set Learning for Robust Mosquito Classification: A Novel Approach to Entomological Studies—0
LaiDA: Linguistics-aware In-context Learning with Data Augmentation for Metaphor Components IdentificationCode0
A Recurrent YOLOv8-based framework for Event-Based Object Detection—0
reCSE: Portable Reshaping Features for Sentence Embedding in Self-supervised Contrastive LearningCode0
Model Debiasing by Learnable Data Augmentation—0
PushPull-Net: Inhibition-driven ResNet robust to image corruptionsCode0
Leveraging Variation Theory in Counterfactual Data Augmentation for Optimized Active Learning—0
Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image SegmentationCode0
Exchangeable Sequence Models Quantify Uncertainty Over Latent Concepts—0
Lisbon Computational Linguists at SemEval-2024 Task 2: Using A Mistral 7B Model and Data AugmentationCode0
Diverse Generation while Maintaining Semantic Coordination: A Diffusion-Based Data Augmentation Method for Object Detection—0
Winning Amazon KDD Cup'24—0
RCDM: Enabling Robustness for Conditional Diffusion Model—0
Label Augmentation for Neural Networks Robustness—0
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution GeneralizationCode0
Symmetric Graph Contrastive Learning against Noisy Views for RecommendationCode0
Trainable Pointwise Decoder Module for Point Cloud Segmentation—0
IAI Group at CheckThat! 2024: Transformer Models and Data Augmentation for Checkworthy Claim DetectionCode0
A Simple Background Augmentation Method for Object Detection with Diffusion Model—0
QuestGen: Effectiveness of Question Generation Methods for Fact-Checking ApplicationsCode0
Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic SystemsCode0
VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections—0
A Model Generalization Study in Localizing Indoor Cows with COw LOcalization (COLO) dataset—0
Exploring Robust Face-Voice Matching in Multilingual Environments—0
More precise edge detectionsCode0
Investigating the Impact of Semi-Supervised Methods with Data Augmentation on Offensive Language Detection in Romanian Language—0
Fusion Self-supervised Learning for Recommendation—0
SpaER: Learning Spatio-temporal Equivariant Representations for Fetal Brain Motion Tracking—0
UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification TasksCode0
Optimizing Synthetic Data for Enhanced Pancreatic Tumor SegmentationCode0
Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition—0
Enhancing Eye Disease Diagnosis with Deep Learning and Synthetic Data Augmentation—0
A Unified Understanding of Adversarial Vulnerability Regarding Unimodal Models and Vision-Language Pre-training Models—0
Utilizing Generative Adversarial Networks for Image Data Augmentation and Classification of Semiconductor Wafer Dicing Induced Defects—0
Domain Generalized Recaptured Screen Image Identification Using SWIN Transformer—0
Deep Spherical SuperpixelsCode0
Enhancing Encrypted Internet Traffic Classification Through Advanced Data Augmentation TechniquesCode0
FairFlow: An Automated Approach to Model-based Counterfactual Data Augmentation For NLPCode0
Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift—0
Refining Corpora from a Model Calibration Perspective for Chinese Spelling Correction—0
MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search—0
Iterative Ensemble Training with Anti-Gradient Control for Mitigating Memorization in Diffusion ModelsCode0
Transformer-based Capacity Prediction for Lithium-ion Batteries with Data Augmentation—0
TADA: Temporal Adversarial Data Augmentation for Time Series Data—0
Scaling Up Single Image Dehazing Algorithm by Cross-Data Vision Alignment for Richer Representation Learning and BeyondCode0
MetaAug: Meta-Data Augmentation for Post-Training QuantizationCode0
Multi-label audio classification with a noisy zero-shot teacherCode0
Data Augmentation in Graph Neural Networks: The Role of Generated Synthetic Graphs—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